MATLAB Supplement to Fuzzy and Neural Approaches in Engineering
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More About This Title MATLAB Supplement to Fuzzy and Neural Approaches in Engineering
English
This book and disk set introduces the fundamentals necessary toapply fuzzy systems, neural networks, and integrated "neurofuzzy"technology to engineering problems using MATLAB. Whether used onits own or as a companion to Fuzzy and Neural Approaches inEngineering by Lefteri H. Tsoukalas and Robert E. Uhrig (Wiley1997), it takes readers step by step from theory to codedevelopment and implementation--enabling students andresearchers to explore the new frontiers in soft computing.
The Supplement features:
* A practical introduction to MATLAB, plus lists of online andother available resources
* MATLAB code demonstrations of theory and architecturesdiscussed in Fuzzy and Neural Approaches in Engineering
* Foundations of fuzzy approaches and relationships, fuzzynumbers, and fuzzy control
* Fundamentals of competitive, associative, and dynamic neuralnetworks and neural control systems
* Practical coverage of neural methods in fuzzy systems and otherhybrid neurofuzzy systems and applications.
System requirements for IBM-compatible disk:
* 486 processor (Pentium recommended)
* 8 MB of RAM (16 MB recommended)
* 5 MB hard disk space
* MATLAB--student or professional edition
* Microsoft Word 6.0 or 7.0.
The Supplement features:
* A practical introduction to MATLAB, plus lists of online andother available resources
* MATLAB code demonstrations of theory and architecturesdiscussed in Fuzzy and Neural Approaches in Engineering
* Foundations of fuzzy approaches and relationships, fuzzynumbers, and fuzzy control
* Fundamentals of competitive, associative, and dynamic neuralnetworks and neural control systems
* Practical coverage of neural methods in fuzzy systems and otherhybrid neurofuzzy systems and applications.
System requirements for IBM-compatible disk:
* 486 processor (Pentium recommended)
* 8 MB of RAM (16 MB recommended)
* 5 MB hard disk space
* MATLAB--student or professional edition
* Microsoft Word 6.0 or 7.0.
English
J. WESLEY HINES, PhD, is a research assistant professor in the Nuclear Engineering Department at the University of Tennessee.
English
Introduction to Hybrid Artificial Intelligence Systems.
FUZZY SYSTEMS: CONCEPTS AND FUNDAMENTALS.
Foundations of Fuzzy Approaches.
Fuzzy Relations.
Fuzzy Numbers.
Linguistic Descriptions and Their Analytical Forms.
Fuzzy Control.
NEURAL NETWORKS: CONCEPTS AND FUNDAMENTALS.
Fundamentals of Neural Networks.
Backpropagation and Related Training Algorithms.
Competitive, Associative, and Other Special Neural Networks.
Dynamic Systems and Neural Control.
Practical Aspects of Using Neural Networks.
INTEGRATED NEURAL-FUZZY TECHNOLOGY.
Fuzzy Methods in Neural Networks.
Fuzzy Methods in Fuzzy Systems.
Selected Hybrid Neurofuzzy Applications.
Dynamic Hybrid Neurofuzzy Systems.
OTHER ARTIFICAL INTELLIGENCE SYSTEMS.
Expert Systems in Neurofuzzy Systems.
Genetic Algorithms.
Epilogue.
Appendix.
Index.
FUZZY SYSTEMS: CONCEPTS AND FUNDAMENTALS.
Foundations of Fuzzy Approaches.
Fuzzy Relations.
Fuzzy Numbers.
Linguistic Descriptions and Their Analytical Forms.
Fuzzy Control.
NEURAL NETWORKS: CONCEPTS AND FUNDAMENTALS.
Fundamentals of Neural Networks.
Backpropagation and Related Training Algorithms.
Competitive, Associative, and Other Special Neural Networks.
Dynamic Systems and Neural Control.
Practical Aspects of Using Neural Networks.
INTEGRATED NEURAL-FUZZY TECHNOLOGY.
Fuzzy Methods in Neural Networks.
Fuzzy Methods in Fuzzy Systems.
Selected Hybrid Neurofuzzy Applications.
Dynamic Hybrid Neurofuzzy Systems.
OTHER ARTIFICAL INTELLIGENCE SYSTEMS.
Expert Systems in Neurofuzzy Systems.
Genetic Algorithms.
Epilogue.
Appendix.
Index.
