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Learning Systems

  • Eduard Aved’yan
  • Editors
  • J. Mason
  • P. C. Parks

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Eduard Aved’yan
    Pages 1-15
  3. Eduard Aved’yan
    Pages 16-39
  4. Eduard Aved’yan
    Pages 62-71
  5. Eduard Aved’yan
    Pages 72-87
  6. Eduard Aved’yan
    Pages 88-100
  7. Eduard Aved’yan
    Pages 110-119
  8. Back Matter
    Pages 121-121

About this book

Introduction

A learning system can be defined as a system which can adapt its behaviour to become more effective at a particular task or set of tasks. It consists of an architecture with a set of variable parameters and an algorithm. Learning systems are useful in many fields, one of the major areas being in control and system identification. This work covers major aspects of learning systems: system architecture, choice of performance index and methods measuring error. Major learning algorithms are explained, including proofs of convergence. Artificial neural networks, which are an important class of learning systems and have been subject to rapidly increasing popularity, are discussed. Where appropriate, examples have been given to demonstrate the practical use of techniques developed in the text. System identification and control using multi-layer networks and CMAC (Cerebellar Model Articulation Controller) are also presented.

Keywords

Adaptive control algorithms artificial neural network artificial neural networks behavior control control system data storage learning learning systems memory neural networks optimization performance system identification

Authors and affiliations

  • Eduard Aved’yan
    • 1
  1. 1.Institute of Control SciencesLaboratory 07Profsoyuznaja 65MoscowRussia

Bibliographic information