Discrete-Time High Order Neural Control

Released on by Edgar N. Sanchez
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  • Discrete-Time High Order Neural Control Book Detail

  • Author : Edgar N. Sanchez
  • Release Date : 2008-04-29
  • Publisher : Springer Science & Business Media
  • Genre : Mathematics
  • Pages : 116
  • ISBN 13 : 3540782885
  • File Size : 17,17 MB
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Discrete-Time High Order Neural Control by Edgar N. Sanchez PDF Summary

Book Description: 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.

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Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control

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Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex sys

Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control

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Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex sys

Discrete-Time Recurrent Neural Control

Discrete-Time Recurrent Neural Control

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The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation