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Item Details
| Title:
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DISCRETE-TIME HIGH ORDER NEURAL CONTROL
TRAINED WITH KALMAN FILTERING |
| By: |
Edgar N. Sanchez, Alma Y. Alanis, Alexander G. Loukianov |
| Format: |
Hardback |
| List price:
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£109.99 |
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We currently do not stock this item, please contact the publisher directly for
further information.
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| ISBN 10: |
3540782885 |
| ISBN 13: |
9783540782889 |
| Publisher: |
SPRINGER-VERLAG BERLIN AND HEIDELBERG GMBH & CO. KG |
| Pub. date: |
1 April, 2008 |
| Edition: |
2008 ed. |
| Series: |
Studies in Computational Intelligence 112 |
| Pages: |
110 |
| Description: |
Intends to present the advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. This book presents solutions for the output trajectory tracking problem of unknown nonlinear systems based on four schemes. |
| Synopsis: |
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. |
| Illustrations: |
4 Tables, black and white; X, 110 p. |
| Publication: |
Germany |
| Imprint: |
Springer-Verlag Berlin and Heidelberg GmbH & Co. K |
| Returns: |
Returnable |
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