Discrete-Time High Order Neural Control: Trained with Kalman Filtering

by Alma Y. Alanis, Alexander G. Loukianov, Edgar N. Sanchez

Book Overview

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.

• Format: Paperback
• Language: English
• ISBN: 3642096956
• ISBN13: 9783642096952
• Release Date: November 2010
• Publisher: Springer
• Length: 110 Pages
• Weight: 0.39 lbs.
• Dimensions: 0.3" x 6.1" x 9.2"