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Discrete-Time High Order Neural Control

Trained with Kalman Filtering

  • Book
  • © 2008

Overview

Authors:
  1. Edgar N. Sanchez

    1. CINVESTAV Unidad Guadalajara, Jalisco, México

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  2. Alma Y. Alanís

    1. Departamento deciencias computacionales, CUCEI Universidad de Guadalajara, Jalisco, Mexico

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    You can also search for this author in PubMed   Google Scholar

  3. Alexander G. Loukianov

    1. CINVESTAV Unidad Guadalajara, Jalisco, México

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  • Presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs
  • Includes supplementary material: sn.pub/extras

Part of the book series: Studies in Computational Intelligence (SCI)

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Table of contents (8 chapters)

  1. Front Matter

    Pages I-X
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  2. Introduction

    Pages 1-4

  3. Mathematical Preliminaries

    Pages 5-10

  4. Discrete-Time Adaptive Neural Backstepping

    Pages 11-28

  5. Discrete-Time Block Control

    Pages 29-43

  6. Discrete-Time Neural Observers

    Pages 45-57

  7. Discrete-Time Output Trajectory Tracking

    Pages 59-72

  8. Real Time Implementation

    Pages 73-94

  9. Conclusions and Future Work

    Pages 95-96

  10. Back Matter

    Pages 97-109
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Keywords

About this book

Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks,controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

Authors and Affiliations

  • CINVESTAV Unidad Guadalajara, Jalisco, México

    Edgar N. Sanchez, Alexander G. Loukianov

  • Departamento deciencias computacionales, CUCEI Universidad de Guadalajara, Jalisco, Mexico

    Alma Y. Alanís

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