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Item Details
Title: NEURAL NETWORK CONTROL OF NONLINEAR DISCRETE-TIME SYSTEMS
By: Jagannathan Sarangapani
Format: Hardback

List price: £210.00
Our price: £189.00
Discount:
10% off
You save: £21.00
ISBN 10: 0824726774
ISBN 13: 9780824726775
Availability: Reprinting. This item may be subject to delays or cancellation.
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Stock: Currently 0 available
Publisher: TAYLOR & FRANCIS INC
Pub. date: 24 April, 2006
Series: Automation and Control Engineering
Pages: 624
Description: Presents modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. This book builds from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, and novel optimal control using the Hamilton-Jacobi-Bellman formulation.
Synopsis: Intelligent systems are a hallmark of modern feedback control systems. But as these systems mature, we have come to expect higher levels of performance in speed and accuracy in the face of severe nonlinearities, disturbances, unforeseen dynamics, and unstructured uncertainties. Artificial neural networks offer a combination of adaptability, parallel processing, and learning capabilities that outperform other intelligent control methods in more complex systems. Borrowing from Biology Examining neurocontroller design in discrete-time for the first time, Neural Network Control of Nonlinear Discrete-Time Systems presents powerful modern control techniques based on the parallelism and adaptive capabilities of biological nervous systems. At every step, the author derives rigorous stability proofs and presents simulation examples to demonstrate the concepts.Progressive Development After an introduction to neural networks, dynamical systems, control of nonlinear systems, and feedback linearization, the book builds systematically from actuator nonlinearities and strict feedback in nonlinear systems to nonstrict feedback, system identification, model reference adaptive control, and novel optimal control using the Hamilton-Jacobi-Bellman formulation. The author concludes by developing a framework for implementing intelligent control in actual industrial systems using embedded hardware. Neural Network Control of Nonlinear Discrete-Time Systems fosters an understanding of neural network controllers and explains how to build them using detailed derivations, stability analysis, and computer simulations.
Illustrations: 171 black & white illustrations, 23 black & white tables
Publication: US
Imprint: Marcel Dekker Inc
Returns: Returnable
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