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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2014

01.10.2014 | Original Article

Adaptive near optimal neural control for a class of discrete-time chaotic system

verfasst von: Li Tang, Ying Gao, Yan-Jun Liu

Erschienen in: Neural Computing and Applications | Ausgabe 5/2014

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Abstract

In this paper, an adaptive critic neural network controller is designed for a class of discrete-time chaotic system. The critic neural network is used to approximate the long-term function. In contrast with the existing results for discrete-time chaotic systems, in this paper, a near optimal control input can be generated when the long-term function is minimized. It is proven that the tracking error, the adaptation laws and the control input are uniformly bounded. A simulation example is employed to illustrate the effectiveness of the proposed algorithm.
Vorheriger Artikel Modeling and analysis of departure routine in air traffic control based on Petri nets
Nächster Artikel Application of Markov chains on image enhancement

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Metadaten
Titel
Adaptive near optimal neural control for a class of discrete-time chaotic system
verfasst von
Li Tang
Ying Gao
Yan-Jun Liu
Publikationsdatum
01.10.2014
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1595-z

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