Neurofuzzy Adaptive Modelling and Control

Neurofuzzy Adaptive Modelling and Control

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The drive for autonomy in manufacturing is making increasing demands on control systems, both for improved performance and extra flexibility. Traditional control systems generally make infeasible assumptions which limit their application, therefore current research has concentrated on intelligent control techniques in order to make systems flexible and robust. This book provides a unified description of several adaptive neural and fuzzy networks and introduces the associate memory class of systems, which describe the similarities and differences existing between fuzzy and neural algorithms. Three networks are desctibed in detail - the Albus CMAC, the B-spline network and a class of fuzzy systems - and then analyzed, their desirable features (local learning, linearly dependent on the parameter set, fuzzy interpretation) are emphasized and the algorithms are all evaluated on a common time series prediction more

Product details

  • Hardback | 528 pages
  • 178 x 235 x 33mm | 972g
  • Pearson Education Limited
  • Prentice-Hall
  • Harlow, United Kingdom
  • English
  • 0131344536
  • 9780131344532

Table of contents

An introduction to learning modelling and control; neural networks for modelling and control; associative memory networks; adaptive linear modelling; instantaneous gradient descent; the CMAC algorithm; the modelling capabilities of the binary CMAC; adaptive B-spline networks; contrained trajectory generation using B-splines; learning fuzzy systems; discussion and further research; modified error correction rule; improved CMAC displacement tables; associative memory network software structure; fuzzy conjunction; weight to rule confidence vector more