MATLAB Supplement to Fuzzy and Neural Approaches in Engineering

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.Die ingenieurtechnische Anwendung von Fuzzy Logic und neuronalen Netzen hat in letzter Zeit immer mehr an Bedeutung gewonnen. Dieses Handbuch enthält auf MATLAB basierende ergänzende Programme zu diesen Themen. Die beiliegende Diskette mit zugehörigen Programmtexten rundet das Werk ab. 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. Aus dem Inhalt:
Introduction to Hybrid Artificial Intelligent Systems;
Foundations of Fuzzy Approaches;
Fuzzy Relationships;
Fuzzy Numbers;
Linguistic Descriptions and their Analytical Form;
Fuzzy Control;
Fundamental of Neural Networks;
Backpropagation and Related Training Paradigms;
Competitive, Associate and other Special Neural Networks;
Dynamic Neural Networks and Control Systems;
Practical Aspects of Neural Networks;
Neural Methods in Fuzzy Systems Neural Methods in Fuzzy Systems;
General Hybrid Neurofuzzy Applications;
Dynamic Hybrid Neurofuzzy Systems;
Role of Expert Systems in Neurofuzzy Systems;
Genetic Algorithms. J. WESLEY HINES, PhD, is a research assistant professor in the Nuclear Engineering Department at the University of Tennessee. This book and disk set introduces the fundamentals necessary to apply fuzzy systems, neural networks, and integrated "neurofuzzy" technology to engineering problems using MATLAB. Whether used on its own or as a companion to Fuzzy and Neural Approaches in Engineering by Lefteri H. Tsoukalas and Robert E. Uhrig (Wiley 1997), it takes readers step by step from theory to code development and implementation—enabling students and researchers to explore the new frontiers in soft computing.

The Supplement features:

• A practical introduction to MATLAB, plus lists of online and other available resources
• MATLAB code demonstrations of theory and architectures discussed in Fuzzy and Neural Approaches in Engineering
• Foundations of fuzzy approaches and relationships, fuzzy numbers, and fuzzy control
• Fundamentals of competitive, associative, and dynamic neural networks and neural control systems
• Practical coverage of neural methods in fuzzy systems and other hybrid 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.