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MATLAB Supplement to Fuzzy and Neural Approaches i Supplement +D3 de JW Hines

MATLAB Supplement to Fuzzy and Neural Approaches i Supplement +D3: Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

Autor JW Hines
en Limba Engleză Paperback – 19 iun 1997
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.
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Specificații

ISBN-13: 9780471192473
ISBN-10: 0471192473
Pagini: 224
Dimensiuni: 215 x 282 x 13 mm
Greutate: 0.66 kg
Editura: Wiley
Seria Adaptive and Cognitive Dynamic Systems: Signal Processing, Learning, Communications and Control

Locul publicării:Hoboken, United States

Notă biografică

J. WESLEY HINES, PhD, is a research assistant professor in the Nuclear Engineering Department at the University of Tennessee.

Descriere

Neural networks and fuzzy systems represent two distinct technologies that deal with uncertainty. Researchers are applying neural networks and fuzzy systems in series, from the use of fuzzy inputs and outputs for neural networks to the employment of individual neural networks to quantify the shape of a fuzzy membership function.