| 书目名称 | Discrete-Time High Order Neural Control | | 副标题 | Trained with Kalman | | 编辑 | Edgar N. Sanchez,Alma Y. Alanís,Alexander G. Louki | | 视频video | | | 概述 | Presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs.Includes supplementary material: | | 丛书名称 | Studies in Computational Intelligence | | 图书封面 |  | | 描述 | 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, d | | 出版日期 | Book 2008 | | 关键词 | Discrete Time; Nonlinear system; Tracking; computational intelligence; control; filtering; intelligence; me | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-540-78289-6 | | isbn_softcover | 978-3-642-09695-2 | | isbn_ebook | 978-3-540-78289-6Series ISSN 1860-949X Series E-ISSN 1860-9503 | | issn_series | 1860-949X | | copyright | Springer-Verlag Berlin Heidelberg 2008 |
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发表于 2025-3-21 18:28:51
