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What is Fuzzy Logic

Chapter 43
Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
Fuzzy logic is a problem-solving methodology that is inspired by human decision-making, taking advantage of our ability to reason with vague or approximate data.
Published in Chapter:
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Filipe Quinaz (University of Beira Interior, Portugal), Paulo Fazendeiro (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal), Miguel Castelo-Branco (University of Beira Interior, Portugal), and Pedro Araújo (University of Beira Interior, Portugal & Portuguese Telecommunications Institute (IT), Portugal)
Copyright: © 2013 |Pages: 25
DOI: 10.4018/978-1-4666-3990-4.ch043
Abstract
The automatic drug infusion in medical care environment remains an elusive goal due to the inherent specificities of the biological systems under control and to subtle shortcomings of the current models. The central aim of this chapter is to present an overview of soft computing techniques and systems that can be used to ameliorate those problems. The applications of control systems in modern medicine are discussed along with several enabling methodologies. The advantages and limitations of automatic drug infusion systems are analyzed. In order to comprehend the evolution of these systems and identify recent advances and research trends, a survey on the hypertension control problem is provided. For illustration, a state-of-the-art automatic drug infusion controller of Sodium Nitroprusside for the mean arterial pressure is described in detail. The chapter ends with final remarks on future research directions towards a fully automated drug infusion system.
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More Results
Chapter 43
Soft Methods for Automatic Drug Infusion in Medical Care Environment
Fuzzy logic is a problem-solving methodology that is inspired by human decision-making, taking advantage of our ability to reason with vague or approximate data.
Published in Chapter: Soft Methods for Automatic Drug Infusion in Medical Care Environment; From: Handbook of Research on ICTs and Management Systems for Improving Efficiency in Healthcare and Social Care
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Chapter 9
Artificial Intelligence Methods and Their Applications in Civil Engineering
A form of many-valued logic that deals with reasoning that is approximate rather than fixed and exact.
Published in Chapter: Artificial Intelligence Methods and Their Applications in Civil Engineering; From: Handbook of Research on Recent Developments in Materials Science and Corrosion Engineering Education
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Chapter 7
Fuzzy Logic in Health Services: Integrated Fuzzy Method for Multi-Criteria Inventory Classification
It is a form of multi-valued logic obtained from fuzzy set theory to deal with reasoning that is approximate rather than precise.
Published in Chapter: Fuzzy Logic in Health Services: Integrated Fuzzy Method for Multi-Criteria Inventory Classification; From: Computational Intelligence and Soft Computing Applications in Healthcare Management Science
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Chapter 3
Reinforcement Learning in Social Media Marketing
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Chapter 11
Application of Fuzzy Logic for Mapping the Agro-Ecological Zones
A form of mathematical logic that employ the concept of partial truth in which the truth can be represented as continuous values between 0 and 1.
Published in Chapter: Application of Fuzzy Logic for Mapping the Agro-Ecological Zones; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 7
Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization
It is a form of many-valued logic which deals with reasoning that is approximate rather than fixed and exact.
Published in Chapter: Application of Soft Computing Techniques for Renewable Energy Network Design and Optimization; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 16
Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques
Logic which presumes possible membership to more than one category with degree of membership, and which is opposite to (exact) crisp logic.
Published in Chapter: Advanced Portfolio Management in Big Data Environments With Machine Learning and Advanced Analytical Techniques; From: Handbook of Research on New Investigations in Artificial Life, AI, and Machine Learning
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Chapter 29
A Fuzzy Simulated Evolution Algorithm for Hard Problems
A form of many-valued logic that deals with approximate rather fixed or exact reasoning.
Published in Chapter: A Fuzzy Simulated Evolution Algorithm for Hard Problems; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 17
A Novel Fuzzy Logic Classifier for Classification and Quality Measurement of Apple Fruit
Fuzzy logic is a problem solving tool of artificial intelligence which deals with approximate reasoning rather than fixed and exact reasoning.
Published in Chapter: A Novel Fuzzy Logic Classifier for Classification and Quality Measurement of Apple Fruit; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 7
A Fuzzy-Based Sustainable Solution for Smart Farming
Fuzzy logic is a computing approach based on multi-valued logic where the variable can take any real number between 0 and 1 as a value based on degree of truthness.
Published in Chapter: A Fuzzy-Based Sustainable Solution for Smart Farming; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 17
Comparing Conventional Methods With Fuzzy Logic for Quantifying Road Congestion: Evidence From Central Kolkata, India
Fuzzy Logic mimics complex human reasoning to arrive at realistic conclusions about reality's imprecise and often fuzzy nature. Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer works.
Published in Chapter: Comparing Conventional Methods With Fuzzy Logic for Quantifying Road Congestion: Evidence From Central Kolkata, India; From: Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0
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Chapter 9
Application of Uncertainty Models in Bioinformatics
Precise definition of this logic does not exist. It is supposed to be the embedded version of fuzzy sets in infinite valued logic. In another sense, according to Zadeh it is equivalent to computing with words.
Published in Chapter: Application of Uncertainty Models in Bioinformatics; From: Handbook of Research on Computational Intelligence Applications in Bioinformatics
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Chapter 3
Fuzzy Rock Mass Rating: Soft-Computing-Aided Preliminary Stability Analysis of Weak Rock Slopes
A multi valued logic system in which elements with uncertain membership boundaries are assigned to a set with hazy boundaries.
Published in Chapter: Fuzzy Rock Mass Rating: Soft-Computing-Aided Preliminary Stability Analysis of Weak Rock Slopes; From: Handbook of Research on Trends and Digital Advances in Engineering Geology
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Chapter 1
The Interdisciplinary Fields of Political Engineering, Public Policy Engineering, Computational Politics, and Computational Public Policy
Fuzzy logic is a field created by Lotfi A. Zadeh for information arising from computational perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries.
Published in Chapter: The Interdisciplinary Fields of Political Engineering, Public Policy Engineering, Computational Politics, and Computational Public Policy; From: Handbook of Research on Politics in the Computer Age
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Chapter 12
Fuzzy-AHP and Fuzzy Saw Application for Sustainable Supply Chain Management
It is the method used to get more realistic results in evaluating the criteria.
Published in Chapter: Fuzzy-AHP and Fuzzy Saw Application for Sustainable Supply Chain Management; From: Considerations on Education for Economic, Social, and Environmental Sustainability
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Chapter 9
Enhanced Fuzzy Assessment Methodology to Find Overlapping in Membership Function Using K Ratio to Find the Yield of Rice
A logically consistent way of reasoning that can cope with uncertain or partial information. Fuzzy logic is characteristic of human thinking and expert systems.
Published in Chapter: Enhanced Fuzzy Assessment Methodology to Find Overlapping in Membership Function Using K Ratio to Find the Yield of Rice; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 119
Fuzzy Logic-Based Classification and Authentication of Beverages
It is a potential tool for reasoning after dealing with imprecision and uncertainty of data set.
Published in Chapter: Fuzzy Logic-Based Classification and Authentication of Beverages; From: Encyclopedia of Data Science and Machine Learning
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Chapter 5
Adaptive Neuro-Fuzzy Systems
Fuzzy logic is an application area of fuzzy set theory dealing with uncertainty in reasoning. It utilizes concepts, principles, and methods developed within fuzzy set theory for formulating various forms of sound approximate reasoning. Fuzzy logic allows for set membership values to range (inclusively) between 0 and 1, and in its linguistic form, imprecise concepts like “slightly”, “quite” and “very”. Specifically, it allows partial membership in a set.
Published in Chapter: Adaptive Neuro-Fuzzy Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 12
Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain
Fuzzy means vagueness. Fuzzy theory is considered as a mathematical tool to handle the uncertainty arising due to vagueness.
Published in Chapter: Intelligence-Based Adaptive Digital Watermarking for Images in Wavelet Transform Domain; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 8
Simulation and Modeling: Design of a Fuzzy Logic Based Hydraulic Turbine Governing System
Is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer is based. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s.
Published in Chapter: Simulation and Modeling: Design of a Fuzzy Logic Based Hydraulic Turbine Governing System; From: Handbook of Research on Computational Simulation and Modeling in Engineering
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Chapter 17
Railway Engineering: Timetable Planning and Control, Artificial Intelligence and Externalities
Is based on observations using decisions based on imprecise and nonnumerical information. Fuzzy models are mathematical equations that represent imprecise information in which the correct values of these variables are real numbers between 0 and 1.
Published in Chapter: Railway Engineering: Timetable Planning and Control, Artificial Intelligence and Externalities; From: Latin American Women and Research Contributions to the IT Field
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Chapter 10
Evaluating IBMEC-RJ’s Intranet Usability Using Fuzzy Logic
Fuzzy logic includes 0 and 1 as extreme cases of truth (or “the state of matters” or “fact”) but also includes the various states of truth in between so that, for example, the result of a comparison between two things could be not “tall” or “short” but 0.38 of tallness.
Published in Chapter: Evaluating IBMEC-RJ’s Intranet Usability Using Fuzzy Logic; From: Handbook of Research on Business Social Networking: Organizational, Managerial, and Technological Dimensions
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Chapter 13
Fuzzy Logic Theory and Applications in Uncertainty Management of Linguistic Evaluations for Students
Fuzzy logic is a field created by Lotfi A. Zadeh for information arising from computational perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries.
Published in Chapter: Fuzzy Logic Theory and Applications in Uncertainty Management of Linguistic Evaluations for Students; From: Cases on Technologies in Education From Classroom 2.0 to Society 5.0
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Chapter 10
The Use of Soft Computing in Management
Deals with reasoning that is approximate rather than fixed and exact.
Published in Chapter: The Use of Soft Computing in Management; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 2
Type-One and Interval Type-Two Fuzzy Logic for Quantitatively Defining Imprecise Linguistic Terms in Politics and Public Policy
Fuzzy logic is a field created by Lotfi A. Zadeh for information arising from computational perception and cognition that is uncertain, imprecise, vague, partially true, or without sharp boundaries.
Published in Chapter: Type-One and Interval Type-Two Fuzzy Logic for Quantitatively Defining Imprecise Linguistic Terms in Politics and Public Policy; From: Handbook of Research on Politics in the Computer Age
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Chapter 4
A Hybrid Fuzzy MCDM Approach for Sustainable Health Tourism Sites Evaluation
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Chapter 131
Incorporating Fuzzy Logic in Data Mining Tasks
A type of logic that recognizes more than simple true and false values. With fuzzy logic, propositions can be represented with degrees of truthfulness and falsehood thus it can deal with imprecise or ambiguous data. Boolean logic is considered to be a special case of fuzzy logic
Published in Chapter: Incorporating Fuzzy Logic in Data Mining Tasks; From: Encyclopedia of Artificial Intelligence
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Chapter 20
A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data
Traditional logic systems assume that things are either in one category or another. Yet in everyday life, we know this is often not precisely so. Fuzzy logic, introduced in the year 1965 by Lofti A. Zadeh, is a mathematical tool for dealing with uncertainty. Unlike Boolean logic, fuzzy logic is multi-valued and handles the concept of partial truth (truth values between “completely true” and “completely false”). Dr. Zadeh states that the principle of complexity and imprecision are correlated: “The closer one looks at a real world problem, the fuzzier becomes its solution”. The fuzzy theory provides a mechanism for representing linguistic constructs such as “high”, “low”, “medium”, “tall”, “many”. In general, fuzzy logic provides an inference structure that enables appropriate human reasoning capabilities. On the contrary, the traditional binary set theory describes crisp events that is, events that either do or do not occur.
Published in Chapter: A Hybrid GA-GSA Algorithm for Optimizing the Performance of an Industrial System by Utilizing Uncertain Data; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 221
Supervised Learning of Fuzzy Logic Systems
Fuzzy sets and Fuzzy Logic were introduced in 1965 by Lotfi Zadeh as a new way to represent vagueness in applications. They are a generalisation of sets in conventional set theory. Fuzzy Logic (FL) aims at modelling imprecise models of reasoning, such as common sense reasoning for uncertain complex processes. A system for representing the meaning of lexically imprecise proposition in natural language structure through the proposition being represented as fuzzy constraints on a variable is provided. Fuzzy logic controllers have been applied to many nonlinear control systems successfully. Linguistic rather than crisp numerical rules are used to control the processes.
Published in Chapter: Supervised Learning of Fuzzy Logic Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 14
Advances in Ultrasound Despeckling: An Overview
The multi-valued logic system, instead of the Boolean logic of ‘0’ or ‘1’, is called fuzzy logic.
Published in Chapter: Advances in Ultrasound Despeckling: An Overview; From: Advanced Classification Techniques for Healthcare Analysis
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Chapter 3
Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics
It is a multivalued logic which incorporates all the possible outcomes in an observation apart from the standard bivalent/bivalued logic commonly dealt with in the conventional computing scenario.
Published in Chapter: Fuzzy Logic: Concepts, System Design, and Applications to Industrial Informatics; From: Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions
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Chapter 20
An On-Line PSO-Based Fuzzy Logic Tuning Approach: Microgrid Frequency Control Case Study
Fuzzy logic is an approach to computing based on degrees of truth rather than the usual true or false Boolean logic on which the modern computer is based. The idea of fuzzy logic was first introduced by Dr. Lotfi Zadeh.
Published in Chapter: An On-Line PSO-Based Fuzzy Logic Tuning Approach: Microgrid Frequency Control Case Study; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 1
An Electric Wheelchair Controlled by Head Movements and Facial Expressions: Uni-Modal, Bi-Modal, and Fuzzy Bi-Modal Modes
Lotfi Zadeh introduced fuzzy logic in 1965. This technique is used to cope with data uncertainty. It involves a fuzzification process, fuzzy inference, and defuzzification process.
Published in Chapter: An Electric Wheelchair Controlled by Head Movements and Facial Expressions: Uni-Modal, Bi-Modal, and Fuzzy Bi-Modal Modes; From: Handbook of Research on Investigations in Artificial Life Research and Development
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Chapter 6
A Neuro-Fuzzy Partner Selection System for Business Social Networks
Fuzzy logic is a form of many-valued logic derived from fuzzy set theory to deal with uncertainty in subjective belief. In contrast with “crisp logic”, where binary sets have two-valued logic, fuzzy logic variables can have a value that ranges between 0 and 1. Furthermore, when linguistic variables are used, these unit-interval numerical values may be described by specific functions.
Published in Chapter: A Neuro-Fuzzy Partner Selection System for Business Social Networks; From: Handbook of Research on Business Social Networking: Organizational, Managerial, and Technological Dimensions
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Chapter 588
Fuzzy Logic Approach in Risk Assessment
A field of study which centers on the human reasoning process and operates by converting it into mathematical functions.
Published in Chapter: Fuzzy Logic Approach in Risk Assessment; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 545
Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot
A precise, linguistic-based method for dealing with approximations derived from imprecise data.
Published in Chapter: Computational Intelligence in Detecting Abnormal Pressure in the Diabetic Foot; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 4
Prediction of International Stock Markets Based on Hybrid Intelligent Systems
It is a type of reasoning designed to mathematically represent uncertainty and vagueness where logical statements are not only true or false. Fuzzy logic is a formalized mathematical tool which is useful to deal with imprecise problems.
Published in Chapter: Prediction of International Stock Markets Based on Hybrid Intelligent Systems; From: Handbook of Research on Innovations in Information Retrieval, Analysis, and Management
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Chapter 15
A BIM Based Application to Support Cost Feasible ‘Green Building' Concept Decisions
Fuzzy logic is a theory that deals with reasoning that is approximate rather than precisely deduced from classical predicate logic. In other words, fuzzy logic deals with well thought out real world expert values in relation to a complex problem.
Published in Chapter: A BIM Based Application to Support Cost Feasible ‘Green Building' Concept Decisions; From: Handbook of Research on Building Information Modeling and Construction Informatics: Concepts and Technologies
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Chapter 3
Breast Ultrasound Image Processing
A logic based on computing degrees of truth rather than the Boolean approach (true or false).
Published in Chapter: Breast Ultrasound Image Processing; From: Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments
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Chapter 6
Providing Healthcare Services in the Virtual Environment
Fuzzy logic is a method of reasoning and resembles human reasoning.
Published in Chapter: Providing Healthcare Services in the Virtual Environment; From: Global Virtual Enterprises in Cloud Computing Environments
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Chapter 13
Application of Fuzzy Logic in Plant Disease Management
Fuzzy logic is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false.
Published in Chapter: Application of Fuzzy Logic in Plant Disease Management; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 276
History of Artificial Intelligence
Techniques for reasoning under uncertainty. It is capable of working with concepts such as ‘thin’, ‘fat’, ‘long’, and ‘short’, if there is no exact data for supporting the decision.
Published in Chapter: History of Artificial Intelligence; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 7
The Technique for Order of Preference by Similarity to Ideal Solution Method in Fuzzy Environment: Fuzzy TOPSIS Method
Fuzzy logic is an approach that defines the belonging of an element to the related set with membership functions rather than 1-0 Boolean logic.
Published in Chapter: The Technique for Order of Preference by Similarity to Ideal Solution Method in Fuzzy Environment: Fuzzy TOPSIS Method; From: Multi-Criteria Decision Analysis in Management
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Chapter 108
Fuzzy Logic Approach in Risk Assessment
A field of study which centers on the human reasoning process and operates by converting it into mathematical functions.
Published in Chapter: Fuzzy Logic Approach in Risk Assessment; From: Advanced Methodologies and Technologies in Business Operations and Management
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Chapter 5
Impulse Noise Filtering: Review of the State-of-the-Art Algorithms for Impulse Noise Filtering
Fuzzy logic is a problem solving tool of artificial intelligence which deals with approximate reasoning rather than fixed and exact reasoning.
Published in Chapter: Impulse Noise Filtering: Review of the State-of-the-Art Algorithms for Impulse Noise Filtering; From: Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing
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Chapter 158
Innovative Formalism for Biological Data Analysis
A mathematic analysis formalism which deal with concepts that cannot be expressed as the “true” or “false” but rather as “partially true” similar to human operators thinking.
Published in Chapter: Innovative Formalism for Biological Data Analysis; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 30
Fuzzy System Dynamics of Manpower Systems
A form of many-valued logic that deals with approximate rather fixed or exact reasoning.
Published in Chapter: Fuzzy System Dynamics of Manpower Systems; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 107
Computer-Aided Diagnosis of Cardiac Arrhythmias
Derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic.
Published in Chapter: Computer-Aided Diagnosis of Cardiac Arrhythmias; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 46
Current Issues and Future Trends of Clinical Decision Support Systems (CDSS)
Based on fuzzy set theory, deals with complex systems where reasoning is approximate, due to complexity or incomplete data.
Published in Chapter: Current Issues and Future Trends of Clinical Decision Support Systems (CDSS); From: Encyclopedia of Healthcare Information Systems
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Chapter 102
Fuzzy Approximation of DES State
It is a Knowledge representation technique and computing framework whose approach is based on degrees of truth rather than the usual “true” or “false” of classical logic.
Published in Chapter: Fuzzy Approximation of DES State; From: Encyclopedia of Artificial Intelligence
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Chapter 1
Fuzzy Expert System in Agriculture Domain
Fuzzy logic is multi-valued and handles the concept of partial truth. A system of logic developed for representing conditions that cannot be easily described by the binary terms “true” and “false.” The concept was introduced by Lotfi Zadeh in 1965.
Published in Chapter: Fuzzy Expert System in Agriculture Domain; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 511
Kinect Applications in Healthcare
A form of many-valued logic. Fuzzy logic deals with reasoning that is approximate rather than fixed and exact. Compared to traditional true or false values, fuzzy logic variables may have a truth value that ranges in degree from 0 to 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.
Published in Chapter: Kinect Applications in Healthcare; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 6
Energy Sustainability of Countries
A type of mathematical logic that handles incomplete knowledge and inexact data via propositions that are true with varying degrees, ranging from totally true to totally false.
Published in Chapter: Energy Sustainability of Countries; From: Handbook of Research on Sustainable Development and Economics
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Chapter 18
Triangular and Trapezoidal Fuzzy Assessment Models
A logic that, in contrast to the classical bivalent logic (yes – no), characterizes a case with multiple values. It is based on the concept of fuzzy set.
Published in Chapter: Triangular and Trapezoidal Fuzzy Assessment Models; From: Handbook of Research on Generalized and Hybrid Set Structures and Applications for Soft Computing
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Chapter 103
Fuzzy Control Systems: An Introduction
A logic that takes on continuous values in between 0 and 1.
Published in Chapter: Fuzzy Control Systems: An Introduction; From: Encyclopedia of Artificial Intelligence
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Chapter 14
Engineering of Experience Based Trust for E-Commerce
conceived by Lotfi Zadeh in 1965, is a mathematical technique for dealing with imprecise and fuzzy data/knowledge and problems that have many solutions rather than one. Although it is implemented in computers which ultimately make only yes-no decisions, fuzzy logic works with ranges of values, solving problems in a way that more resembles human logic. Fuzzy logic is used for solving problems with expert systems and other intelligent systems that must react to an imperfect environment of highly variable, volatile or unpredictable conditions (Zimmermann, 1996).
Published in Chapter: Engineering of Experience Based Trust for E-Commerce; From: Handbook of Research on Complex Dynamic Process Management: Techniques for Adaptability in Turbulent Environments
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Chapter 16
Hybrid Intelligence Framework for Improvement of Information Security of Critical Infrastructures
Fuzzy Logic is a form of mathematical logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.
Published in Chapter: Hybrid Intelligence Framework for Improvement of Information Security of Critical Infrastructures; From: Handbook of Research on Cyber Crime and Information Privacy
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Chapter 1
Benchmarking of the Maintenance Service in Health Care Organizations
A technique of computational intelligence introduced by Lotfy A. Zadeh in 1965 which allows the imprecision, uncertainty, vagueness, etc., which characterize human judgements and thought to be included. It represents knowledge, which is primarily linguistic and qualitative, in mathematical language, by the use of fuzzy sets and associated characteristic functions.
Published in Chapter: Benchmarking of the Maintenance Service in Health Care Organizations; From: Handbook of Research on Data Science for Effective Healthcare Practice and Administration
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Chapter 18
Data Analytics in Industry 4.0: In the Perspective of Big Data
A form of explanations of crisp values to 0-1 values in order to represent the “truth” via determining membership function.
Published in Chapter: Data Analytics in Industry 4.0: In the Perspective of Big Data; From: Handbook of Research on Applied Optimization Methodologies in Manufacturing Systems
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Chapter 18
Ant Colony Optimization for Use in Content Based Image Retrieval
Is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem.
Published in Chapter: Ant Colony Optimization for Use in Content Based Image Retrieval; From: Handbook of Research on Artificial Immune Systems and Natural Computing: Applying Complex Adaptive Technologies
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Chapter 25
Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries
Logic which presumes possible membership to more than one category with degree of membership, and which is opposite to (exact) crisp logic.
Published in Chapter: Early Warning System Framework Proposal Based on Structured Analytical Techniques, SNA, and Fuzzy Expert System for Different Industries; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 15
Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems
An approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic. The idea of fuzzy logic was first advanced by Dr. Lotfi Zadeh of the University of California at Berkeley in the 1960s.
Published in Chapter: Thinking eHealth: Empowering for Wellbeing With Health Monitoring Systems; From: Strategic Innovations and Interdisciplinary Perspectives in Telecommunications and Networking
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Chapter 5
A Fuzzy-Based Calorie Burn Calculator for a Gamified Walking Activity Using Treadmill
A computing paradigm which computes the degree of truth rather than “true” or “false” in contrast to the Boolean logic in which the true or false values of variables can be only 1 or 0.
Published in Chapter: A Fuzzy-Based Calorie Burn Calculator for a Gamified Walking Activity Using Treadmill; From: Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making
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Chapter 5
Optimum Allocation of Transmission Technologies for Solving the BTS Interconnection Problem in Cellular Systems
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Chapter 10
Optimizing WSNs for CPS Using Machine Learning Techniques
Fuzzy logic is not the standard “true or false” approach; it is rather focused on “degrees of fact.”
Published in Chapter: Optimizing WSNs for CPS Using Machine Learning Techniques; From: Artificial Intelligence Paradigms for Smart Cyber-Physical Systems
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Chapter 29
A Framework Proposal to Assess the LARG Index of a Supply Chain in a Fuzzy Context
Branch of logic especially well suited to tackle problems defined in uncertain and imprecise environments due to its inherent capability to deal and reason with (vague) linguistic terms and incomplete data.
Published in Chapter: A Framework Proposal to Assess the LARG Index of a Supply Chain in a Fuzzy Context; From: Handbook of Research on Enterprise 2.0: Technological, Social, and Organizational Dimensions
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Chapter 91
Using Fuzzy Logic for Optimizing Business Intelligence Success in Multiple Investment Combinations
Take into account ambiguous cases or exceptions in natural language and progressively incorporate them into the expertise. It allows for vague boundaries, provides a mechanism to utilize fuzziness in subjective or imprecise determinations of preferences, constraints, and goals.
Published in Chapter: Using Fuzzy Logic for Optimizing Business Intelligence Success in Multiple Investment Combinations; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 17
Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries
Logic which presumes possible membership to more than one category with degree of membership, and which is opposite to (exact) crisp logic
Published in Chapter: Data Mining Models as a Tool for Churn Reduction and Custom Product Development in Telecommunication Industries; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 19
A Web Metadata Based-Model for Information Quality Prediction
is derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well-thought-out real-world expert values for a complex problem (Klir & Yuan, 1995).
Published in Chapter: A Web Metadata Based-Model for Information Quality Prediction; From: Handbook of Research on Web Information Systems Quality
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Chapter 8
Fuzzy System Dynamics: An Application to Supply Chain Management
A form of many-valued logic that deals with approximate rather fixed or exact reasoning.
Published in Chapter: Fuzzy System Dynamics: An Application to Supply Chain Management; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 15
Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications
Fuzzy logic provides a way of taking our commonsense knowledge that most things are a matter of degree into account when a computer is automatically making a decision.
Published in Chapter: Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications; From: Handbook of Research on Solar Energy Systems and Technologies
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Chapter 12
A Survey on Different Digital Authentication Systems Using Fuzzy Logic
Fuzzy logic is a special kind of soft computing tool and it is applicable in the field of artificial intelligence effectively.
Published in Chapter: A Survey on Different Digital Authentication Systems Using Fuzzy Logic; From: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management
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Chapter 107
Fuzzy Logic Estimator for Variant SNR Environments
Fuzzy logic was derived from Fuzzy Set theory, working with a reason that it is approximate rather than precise, deducted from the typical predicate logic.
Published in Chapter: Fuzzy Logic Estimator for Variant SNR Environments; From: Encyclopedia of Artificial Intelligence
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Chapter 3
Fuzzy Hybrid FMEA for Risk Assessment in Service Industry: An Integrated Intuitionistic Fuzzy AHP and TOPSIS Approach
A form of multi-valued logic obtained from the fuzzy set theory to deal with reasoning that is approximate rather than precise.
Published in Chapter: Fuzzy Hybrid FMEA for Risk Assessment in Service Industry: An Integrated Intuitionistic Fuzzy AHP and TOPSIS Approach; From: Advancements in Fuzzy Reliability Theory
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Chapter 2
A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques
A form of explanations of crisp values to 0-1 values in order to represent the “truth” via determining membership function.
Published in Chapter: A Novel Approach to Segmentation Using Customer Locations Data and Intelligent Techniques; From: Handbook of Research on Intelligent Techniques and Modeling Applications in Marketing Analytics
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Chapter 70
Designing Unsupervised Hierarchical Fuzzy Logic Systems
Fuzzy sets and Fuzzy Logic were introduced in 1965 by Lotfi Zadeh as a new way to represent vagueness in applications. They are a generalisation of sets in conventional set theory. Fuzzy Logic (FL) aims at modelling imprecise models of reasoning, such as common sense reasoning for uncertain complex processes. A system for representing the meaning of lexically imprecise proposition in natural language structure through the proposition being represented as fuzzy constraints on a variable is provided. Fuzzy logic controllers have been applied to many nonlinear control systems successfully. Linguistic rather than crisp numerical rules are used to control the processes
Published in Chapter: Designing Unsupervised Hierarchical Fuzzy Logic Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 18
Automated Framework for Software Process Model Selection Based on Soft Computing Approach
A way of reasoning that resembles human reasoning. The methodology of FL impersonates the method of decision making in humans that involves all intermediate possibilities between digital values YES and NO.
Published in Chapter: Automated Framework for Software Process Model Selection Based on Soft Computing Approach; From: Handbook of Research on Fuzzy and Rough Set Theory in Organizational Decision Making
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Chapter 237
Watermarking Using Artificial Intelligence Techniques
It is an art of approximation technique to simulate human brain activities.
Published in Chapter: Watermarking Using Artificial Intelligence Techniques; From: Encyclopedia of Business Analytics and Optimization
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Chapter 14
Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology
Traditional logic systems assume that things are either in one category or another. Yet in everyday life, we know this is often not precisely so. Fuzzy logic, introduced in the year 1965 by Lofti A. Zadeh, is a mathematical tool for dealing with uncertainty. Unlike Boolean logic, fuzzy logic is multi-valued and handles the concept of partial truth (truth values between completely true and completely false). Dr. Zadeh states that the principle of complexity and imprecision are correlated: The closer one looks at a real world problem, the fuzzier becomes its solution. The fuzzy theory provides a mechanism for representing linguistic constructs such as high, low, medium, tall, many. In general, fuzzy logic provides an inference structure that enables appropriate human reasoning capabilities. On the contrary, the traditional binary set theory describes crisp events that is, events that either do or do not occur.
Published in Chapter: Predicting Uncertain Behavior and Performance Analysis of the Pulping System in a Paper Industry using PSO and Fuzzy Methodology; From: Handbook of Research on Novel Soft Computing Intelligent Algorithms: Theory and Practical Applications
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Chapter 31
Application of Fuzzy Logic to Fraud Detection
A mathematical technique that classifies subjective reasoning and assigns data to a particular group, or cluster, based on the degree of possibility the data has of being in that group.
Published in Chapter: Application of Fuzzy Logic to Fraud Detection; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 119
Intelligent Systems to Support Human Decision Making
Artificial intelligence method that can formally represent inputs that are imprecise or uncertain by permitting a range of values.
Published in Chapter: Intelligent Systems to Support Human Decision Making; From: Encyclopedia of Business Analytics and Optimization
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Chapter 28
Computer Intelligence in Healthcare
A form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact. In contrast with traditional logic theory, where binary sets have two-valued logic: true or false, fuzzy logic variables may have a truth value that ranges in degree between 0 and 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.
Published in Chapter: Computer Intelligence in Healthcare; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 9
Fuzzy-Logic-Based Decision Engine for Offloading IoT Application Using Fog Computing
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Chapter 5
Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature
This is a mathematical framework for representing and processing uncertainty and imprecision in information. It was developed as an alternative to classical (or “crisp”) logic, which only deals with binary values of true and false. In fuzzy logic, information is represented by degrees of truth, rather than binary values, allowing for more nuanced and flexible representations of uncertainty. In fuzzy logic, variables can take on values between 0 and 1, representing the degree to which they are true or false. These degrees of truth can be combined using fuzzy rules, which are statements that relate the values of multiple variables to each other. The output of a fuzzy system is a fuzzy set, which represents the degree of membership of the system's output in various possible categories. This output can then be defuzzified, or translated into a crisp value, by using various methods, such as the center of gravity or the mean of maximum.
Published in Chapter: Artificial Intelligence and Marketing: Progressive or Disruptive Transformation? Review of the Literature; From: Confronting Security and Privacy Challenges in Digital Marketing
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Chapter 91
Fuzzy Scale Table: An Effective Research Tool
Fuzzy logic was developed for handling vague and uncertain information by allowing intermediate values between the Boolean values true and false.
Published in Chapter: Fuzzy Scale Table: An Effective Research Tool; From: Encyclopedia of Business Analytics and Optimization
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Chapter 51
Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling
A way of reasoning that can cope with uncertain or partial information.
Published in Chapter: Decision Support Systems for Cardiovascular Diseases Based on Data Mining and Fuzzy Modelling; From: Encyclopedia of Healthcare Information Systems
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Chapter 322
A Survey of Using Microsoft Kinect in Healthcare
A form of many-valued logic. Fuzzy logic deals with reasoning that is approximate rather than fixed and exact. Compared to traditional true or false values, fuzzy logic variables may have a truth value that ranges in degree from 0 to 1. Fuzzy logic has been extended to handle the concept of partial truth, where the truth value may range between completely true and completely false.
Published in Chapter: A Survey of Using Microsoft Kinect in Healthcare; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 26
Computational Intelligence for Pathological Issues in Precision Agriculture
A self-learning technique that is used to handle uncertainty, ambiguity and vagueness. It provides a means of translating qualitative and imprecise information into quantitative (linguistic) terms.
Published in Chapter: Computational Intelligence for Pathological Issues in Precision Agriculture; From: Handbook of Research on Computational Intelligence for Engineering, Science, and Business
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Chapter 8
SERVQUAL-Based Evaluation of Service Quality of Energy Companies in Turkey: Strategic Policies for Sustainable Economic Development
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Chapter 7
Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture
A computing method uses “degrees of truth” logic rather than the usual “true or false” Boolean logic.
Published in Chapter: Applications of Data Mining Techniques in Smart Farming for Sustainable Agriculture; From: Modern Techniques for Agricultural Disease Management and Crop Yield Prediction
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Chapter 26
Time Constraints for Sellers in Electronic Markets
Mathematical technique for dealing with imprecise or incomplete information in a specified scenario.
Published in Chapter: Time Constraints for Sellers in Electronic Markets; From: Encyclopedia of E-Business Development and Management in the Global Economy
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Chapter 3
Dynamic Difficulty Adjustment (DDA) on a Serious Game Used for Hand Rehabilitation
It was introduced by Lotfi Zadeh in 1965. Fuzzy logic could be employed to tackle data uncertainty through mainly three processes: fuzzification, fuzzy inference, and defuzzification.
Published in Chapter: Dynamic Difficulty Adjustment (DDA) on a Serious Game Used for Hand Rehabilitation; From: Perspectives and Considerations on the Evolution of Smart Systems
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Chapter 61
Introduction to Fuzzy Logic and Fuzzy Linear Programming
Fuzzy logic is a mathematical technique for dealing with imprecise data and problems that have many solutions rather than one.
Published in Chapter: Introduction to Fuzzy Logic and Fuzzy Linear Programming; From: Encyclopedia of Decision Making and Decision Support Technologies
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Chapter 8
Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets
Logic which presumes possible membership to more than one category with degree of membership, and which is opposite to (exact) crisp logic.
Published in Chapter: Discovering Behavioural Patterns within Customer Population by using Temporal Data Subsets; From: Handbook of Research on Advanced Hybrid Intelligent Techniques and Applications
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Chapter 16
Data-Driven Decision Making to Select Condition-Based Maintenance Technology
A technique of computational intelligence introduced by Zadeh in 1965 used when the complexity of a decision is high and affected by the incomplete knowledge, uncertainty or vagueness which is characteristic of human judgements and thought. It represents knowledge, which is primarily linguistic and qualitative, by the use of fuzzy sets defined by a membership function and using fuzzy operators to perform mathematical operations.
Published in Chapter: Data-Driven Decision Making to Select Condition-Based Maintenance Technology; From: Handbook of Research on Industrial Advancement in Scientific Knowledge
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Chapter 90
Product Evaluation Services for E-Commerce
An extension of Boolean logic that deals with the concept of partial. It is useful in clustering, expert systems and other intelligent system applications.
Published in Chapter: Product Evaluation Services for E-Commerce; From: Encyclopedia of Information Communication Technology
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Chapter 2
Fuzzy Ontology for Requirements Determination and Documentation During Software Development
It is a multi-valued logic based on sets without boundary and offers graded membership of an element to such set. Crisp logic always gives binary values say 0 or 1; however, the fuzzy logic provides many values between 0 and 1.
Published in Chapter: Fuzzy Ontology for Requirements Determination and Documentation During Software Development; From: Tools and Techniques for Software Development in Large Organizations: Emerging Research and Opportunities
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Chapter 120
Hierarchical Neuro-Fuzzy Systems Part I
Can be used to translate, in mathematical terms, the imprecise information expressed by a set of linguistic IF-THEN rules. Fuzzy Logic studies the formal principles of approximate reasoning and is based on Fuzzy Set Theory. It deals with intrinsic imprecision, associated with the description of the properties of a phenomenon, and not with the imprecision associated with the measurement of the phenomenon itself. While classical logic is of a bivalent nature (true or false), fuzzy logic admits multivalence
Published in Chapter: Hierarchical Neuro-Fuzzy Systems Part I; From: Encyclopedia of Artificial Intelligence
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Chapter 118
Handling Fuzzy Similarity for Data Classification
An extension of Boolean logic dealing with the concept of partial truth. Fuzzy logic replaces Boolean truth values (0 or 1, black or white, yes or no) with degrees of truth
Published in Chapter: Handling Fuzzy Similarity for Data Classification; From: Encyclopedia of Artificial Intelligence
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Chapter 9
Contemporary Visual Identities: Mutant Brands
Is the logic of live, illogical; partial truth; is a form of many-valued logic in which the truth values of variables may be any real number between 0 and 1 both inclusive.
Published in Chapter: Contemporary Visual Identities: Mutant Brands; From: Handbook of Research on Driving Industrial Competitiveness With Innovative Design Principles
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Chapter 6
Cultural Indoctrination and Open Innovation in Human Creativity
Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) Boolean logic on which the modern computer is based.
Published in Chapter: Cultural Indoctrination and Open Innovation in Human Creativity; From: Applied Psycholinguistics and Multilingual Cognition in Human Creativity
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Chapter 90
Fuzzy MCDM Approach for Make or Buy Decision Problem
Apart from Boolean logic, can handle imprecise and uncertainties.
Published in Chapter: Fuzzy MCDM Approach for Make or Buy Decision Problem; From: Encyclopedia of Business Analytics and Optimization
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Chapter 22
Fuzzy Logic in Healthcare
It is the multivalued logic, attempting to emulate human reasoning in the computer applications based on degree of truth rather than classical computer true or false approach.
Published in Chapter: Fuzzy Logic in Healthcare; From: Handbook of Research on Artificial Intelligence Techniques and Algorithms
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Chapter 43
Computational Biology
Fuzzy logic breaks input into variables and assigns each input a probability of being correct on a scale of 0 to 1 with 0 being false This is different from classical discrete computational systems which only allow inputs of false (0) and true (1).
Published in Chapter: Computational Biology; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 7
Trust Calculation Using Fuzzy Logic in Cloud Computing
It is a type of logic which deals with incomplete, vague and imprecise information. It uses degrees of truth rather than the precise values of 0 or 1 to arrive at a conclusion.
Published in Chapter: Trust Calculation Using Fuzzy Logic in Cloud Computing; From: Handbook of Research on Security Considerations in Cloud Computing
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Chapter 3
Principles of a Hybrid Intelligence Framework for Augmented Analytics
Fuzzy Logic is a form of mathematical logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.
Published in Chapter: Principles of a Hybrid Intelligence Framework for Augmented Analytics; From: Handbook of Research on Foundations and Applications of Intelligent Business Analytics
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Chapter 16
Hybrid Computational Intelligence
A form of many-valued logic which deals with approximate reasoning.
Published in Chapter: Hybrid Computational Intelligence; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 2
Maximum Likelihood-Based Fuzzy Adaptive Kalman Filter Applied to State Estimation of Permanent Magnet Synchronous Motors
It is a logical form of many values, represented by an approximate reasoning whose structures is based on rules. The rule base defines the require outputs for any given combinations of inputs.It is used in a wide variety of applicationssuch engineering, economics, medicine, among others.
Published in Chapter: Maximum Likelihood-Based Fuzzy Adaptive Kalman Filter Applied to State Estimation of Permanent Magnet Synchronous Motors; From: Applications of Artificial Intelligence in Electrical Engineering
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Chapter 38
Map Matching Algorithms for Intelligent Transport Systems
Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth i.e., truth values between “completely true” and “completely false”. It was introduced in the 1960’s by Zadeh (1965). It is suitable to deal with problems involving knowledge expressed in vague, and linguistic terms.
Published in Chapter: Map Matching Algorithms for Intelligent Transport Systems; From: Handbook of Research on Geoinformatics
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Chapter 129
Nature-Inspired Cooperative Strategies for Optimization
Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth (truth values between “completely true” and “completely false”). It was introduced by Dr. Zadeh in the 1960’s as a means to model the uncertainty of natural language. (Zadeh, 1965).
Published in Chapter: Nature-Inspired Cooperative Strategies for Optimization; From: Encyclopedia of Networked and Virtual Organizations
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Chapter 19
Application of Fuzzy User's Profile for Mining Reusable E-Learning Repositories on Web through Lightweight Mobile Agent
Fuzzy logic is a multi valued logic based on fuzzy sets. This type of logic is very nearer to the way how humans identify and categorize things into the classes whose boundaries are not fixed.
Published in Chapter: Application of Fuzzy User's Profile for Mining Reusable E-Learning Repositories on Web through Lightweight Mobile Agent; From: Handbook of Research on Innovative Database Query Processing Techniques
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Chapter 4
Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT)
It is a subset of DL which uses a vast spectrum of data and a heuristic plan of action, to reach an accurate conclusion (McNeill, 2014 AU257: The in-text citation "McNeill, 2014" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).
Published in Chapter: Cancer Diagnosis Using Artificial Intelligence (AI) and Internet of Things (IoT); From: Revolutionizing Healthcare Through Artificial Intelligence and Internet of Things Applications
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Chapter 2
Hybrid Intelligence Framework for Augmented Analytics
Fuzzy Logic is a form of mathematical logic in which the truth values of variables may be any real number between 0 and 1. It is employed to handle the concept of partial truth, where the truth value may range between completely true and completely false. By contrast, in Boolean logic, the truth values of variables may only be the integer values 0 or 1.
Published in Chapter: Hybrid Intelligence Framework for Augmented Analytics; From: Intelligent Analytics With Advanced Multi-Industry Applications
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Chapter 12
Agricultural Health and Safety Measures by Fuzzy ahp and Prediction by Fuzzy Expert System: Agricultural Risk Factor
Fuzzy logic is multi-valued and handles the concept of partial truth. A system of logic developed for representing conditions that cannot be easily described by the binary terms “true” and “false.”
Published in Chapter: Agricultural Health and Safety Measures by Fuzzy ahp and Prediction by Fuzzy Expert System: Agricultural Risk Factor; From: Fuzzy Expert Systems and Applications in Agricultural Diagnosis
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Chapter 17
Navigation Control of a Mobile Robot under Time Constraint using Genetic Algorithms, CSP Techniques, and Fuzzy Logic
A form of many-valued logic; it deals with reasoning that is approximate rather than fixed and exact.
Published in Chapter: Navigation Control of a Mobile Robot under Time Constraint using Genetic Algorithms, CSP Techniques, and Fuzzy Logic; From: Handbook of Research on Advanced Intelligent Control Engineering and Automation
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Chapter 13
Fuzzy Failure Mode and Effects Analysis in a Pharmaceutical Production Process With Fuzzy PROMETHEE Method
It is a form of multi-valued logic obtained from fuzzy set theory to deal with reasoning that is approximate rather than precise.
Published in Chapter: Fuzzy Failure Mode and Effects Analysis in a Pharmaceutical Production Process With Fuzzy PROMETHEE Method; From: Multi-Criteria Decision Analysis in Management
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Chapter 140
Model for Sustainability in Healthcare Organizations
Introduced by Lotfy A. Zadeh in 1965, it is a technique of computational intelligence which facilitates working with imprecise data, or data which are not well defined. The elements which human thought is based on are not numbers but linguistic tokens. Thus, knowledge, which is mainly qualitative and linguistic, can be represented in mathematical language by diffuse sets and characteristic functions associated with them.
Published in Chapter: Model for Sustainability in Healthcare Organizations; From: Encyclopedia of Business Analytics and Optimization
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Chapter 1
Introduction and Trends to Fuzzy Logic and Fuzzy Databases
Fuzzy logic is derived from fuzzy set theory by Zadeh (1965), dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem.
Published in Chapter: Introduction and Trends to Fuzzy Logic and Fuzzy Databases; From: Handbook of Research on Fuzzy Information Processing in Databases
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Chapter 218
Stream Processing of a Neural Classifier I
Mathematical method originated from the fuzzy set theory, which allows the partial membership of elements in a set, dealing with approximate reasoning instead of exactly deduced from classical logic.
Published in Chapter: Stream Processing of a Neural Classifier I; From: Encyclopedia of Artificial Intelligence
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Chapter 143
Intuitionistic Fuzzy Image Processing
Fuzzy logic is an extension of traditional Boolean logic. It is derived from fuzzy set theory and deals with concepts of partial truth and reasoning that is approximate rather than precise.
Published in Chapter: Intuitionistic Fuzzy Image Processing; From: Encyclopedia of Artificial Intelligence
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Chapter 60
Interfaces Usability for Monitoring Systems
Fuzzy logic is multi-valued logic for reasoning that can cope with uncertain or partial information; characteristic of human thinking and inference/expert systems.
Published in Chapter: Interfaces Usability for Monitoring Systems; From: Encyclopedia of Decision Making and Decision Support Technologies
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Chapter 82
A Fuzzy Multi-Agent System for Combinatorial Optimization
Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth (truth values between “completely true” and “completely false”). It was introduced by Zadeh in the 1960s as a means to model the uncertainty of natural language (Zadeh, 1965).
Published in Chapter: A Fuzzy Multi-Agent System for Combinatorial Optimization; From: Encyclopedia of Networked and Virtual Organizations
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Chapter 104
Computational Biology
Fuzzy logic breaks input into variables and assigns each input a probability of being correct on a scale of 0 to 1 with 0 being false This is different from classical discrete computational systems which only allow inputs of false (0) and true (1).
Published in Chapter: Computational Biology; From: Encyclopedia of Information Science and Technology, Second Edition
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Chapter 28
Multi-Tier Knowledge-Based System Accessing Learning Object Repository Using Fuzzy XML
Fuzzy logic is a multi-valued logic based on fuzzy sets. This type of logic is very nearer to the way how humans are identifying and categorizing things into the classes whose boundaries are not fixed.
Published in Chapter: Multi-Tier Knowledge-Based System Accessing Learning Object Repository Using Fuzzy XML; From: Handbook of Research on Practices and Outcomes in E-Learning: Issues and Trends
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Chapter 40
Cost-Sensitive Classification for Medical Diagnosis
A form of logic where variables can take on variable degrees of truth.
Published in Chapter: Cost-Sensitive Classification for Medical Diagnosis; From: Encyclopedia of Healthcare Information Systems
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Chapter 13
Design of an Agribusiness Innovative and Autonomous Robot System for Chemical Weed Control for Staple Food Crops Production in Sub-Saharan Africa
Fuzzy logic is a method that deals with inaccuracy and indecision. Fuzzy logic imitates the way the human brain works to solve problems, thereby aiding a system to make the right decision in imprecise situations.
Published in Chapter: Design of an Agribusiness Innovative and Autonomous Robot System for Chemical Weed Control for Staple Food Crops Production in Sub-Saharan Africa; From: Opportunities and Strategic Use of Agribusiness Information Systems
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Chapter 22
Artificial Intelligence: Current Issues and Applications
The system of theories utilized in mathematics, computing, and philosophy to deal with the statements that are neither true nor false.
Published in Chapter: Artificial Intelligence: Current Issues and Applications; From: Handbook of Research on Manufacturing Process Modeling and Optimization Strategies
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Chapter 164
Multilayer Optimization Approach for Fuzzy Systems
Type of logic dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic.
Published in Chapter: Multilayer Optimization Approach for Fuzzy Systems; From: Encyclopedia of Artificial Intelligence
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Chapter 180
Neural Networks on Handwritten Signature Verification
Derived from fuzzy set theory dealing with reasoning that is approximate rather than precisely deduced from classical predicate logic. It can be thought of as the application side of fuzzy set theory dealing with well thought out real world expert values for a complex problem.
Published in Chapter: Neural Networks on Handwritten Signature Verification; From: Encyclopedia of Artificial Intelligence
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