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Matlab Fuzzy Logic Toolbox

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Introduction To Fuzzy Logic Using Matlab

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Author : S.N. Sivanandam
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-10-28

Introduction To Fuzzy Logic Using Matlab written by S.N. Sivanandam and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-10-28 with Technology & Engineering categories.

This book provides a broad-ranging, but detailed overview of the basics of Fuzzy Logic. The fundamentals of Fuzzy Logic are discussed in detail, and illustrated with various solved examples. The book also deals with applications of Fuzzy Logic, to help readers more fully understand the concepts involved. Solutions to the problems are programmed using MATLAB 6.0, with simulated results. The MATLAB Fuzzy Logic toolbox is provided for easy reference.

Matlab Fuzzy Logic Toolbox

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Author : J. S. Roger Jang
language : en
Publisher:
Release Date : 1997

Matlab Fuzzy Logic Toolbox written by J. S. Roger Jang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1997 with Expert systems (Computer science) categories.

Fuzzy Logic Toolbox

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Author : Ned Gulley
language : en
Publisher:
Release Date : 1995

Fuzzy Logic Toolbox written by Ned Gulley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995 with Expert systems (Computer science) categories.

Fuzzy Logic With Matlab

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Author : Godfrey H.
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-11-12

Fuzzy Logic With Matlab written by Godfrey H. and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-12 with categories.

Fuzzy Logic Toolbox provides MATLAB functions, graphical tools, and a SimulinkR block for analyzing, designing, and simulating systems based on fuzzy logic. The product guides you through the steps of designing fuzzy inference systems. Functions are provided for many common methods, including fuzzy clustering and adaptive neurofuzzy learning. The toolbox lets you model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. You can use it as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system. The more important features are the next:* Specialized GUIs for building fuzzy inference systems and viewing and analyzing results* Membership functions for creating fuzzy inference systems * Support for AND, OR, and NOT logic in user-defined rules* Standard Mamdani and Sugeno-type fuzzy inference systems* Automated membership function shaping through neuroadaptive and fuzzy clustering learning techniques* Ability to embed a fuzzy inference system in a Simulink model * Ability to generate embeddable C code or stand-alone executable fuzzy inference engines

Fuzzy Logic Toolbox

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Author :
language : en
Publisher:
Release Date : 2001

Fuzzy Logic Toolbox written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Expert systems (Computer science) categories.

Fuzzy Logic With Matlab

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Author : A. Taylor
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2017-11-15

Fuzzy Logic With Matlab written by A. Taylor and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-11-15 with categories.

Fuzzy Logic Toolbox provides MATLAB functions, apps, and a Simulink block for analyzing, designing, and simulating systems based on fuzzy logic. The book guides you through the steps of designing fuzzy inference systems. Functions are provided formany common methods, including fuzzy clustering and adaptive neuro fuzzy learning.The toolbox lets you model complex system behaviors using simple logic rules, and then implement these rules in a fuzzy inference system. You can use it as a stand-alone fuzzy inference engine. Alternatively, you can use fuzzy inference blocks in Simulink and simulate the fuzzy systems within a comprehensive model of the entire dynamic system. The most important features that this Toolbox provides are the following: - Fuzzy Logic Design app for building fuzzy inference systems and viewing andanalyzing results - Membership functions for creating fuzzy inference systems - Support for AND, OR, and NOT logic in user-defined rules - Standard Mamdani and Sugeno-type fuzzy inference systems - Automated membership function shaping through neuroadaptive and fuzzy clusteringlearning techniques - Ability to embed a fuzzy inference system in a Simulink model - Ability to generate embeddable C code or stand-alone executable fuzzy inferenceengines