Discrete Time High Order Neural Control

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Discrete-Time High Order Neural Control

Discrete-Time High Order Neural Control
Author :
Publisher : Springer Science & Business Media
Total Pages : 116
Release :
ISBN-10 : 9783540782889
ISBN-13 : 3540782885
Rating : 4/5 (885 Downloads)

Book Synopsis Discrete-Time High Order Neural Control by : Edgar N. Sanchez

Download or read book Discrete-Time High Order Neural Control written by Edgar N. Sanchez and published by Springer Science & Business Media. This book was released on 2008-04-29 with total page 116 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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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The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation
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In recent years, Higher Order Neural Networks (HONNs) have been widely adopted by researchers for applications in control signal generating, pattern recognition