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

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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.

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Authors and Affiliations

  1. College of Science, Liaoning University of Technology, Jinzhou, 121001, Liaoning, China

    Li Tang, Ying Gao & Yan-Jun Liu

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  1. Li Tang
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  2. Ying Gao
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  3. Yan-Jun Liu
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Correspondence to Ying Gao.

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Tang, L., Gao, Y. & Liu, YJ. Adaptive near optimal neural control for a class of discrete-time chaotic system. Neural Comput & Applic 25, 1111–1117 (2014). https://doi.org/10.1007/s00521-014-1595-z

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  • DOI: https://doi.org/10.1007/s00521-014-1595-z

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