Fuzzy Neural Network Based Predictive Control of Chaotic Nonlinear Systems

Jong Tae CHOI; Yoon Ho CHOI

Fuzzy Neural Network Based Predictive Control of Chaotic Nonlinear Systems

Jong Tae CHOI, Yoon Ho CHOI

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In this paper, we present a predictive control method, based on Fuzzy Neural Network (FNN), for the control of chaotic systems without precise mathematical models. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of the FNN are determined adaptively throughout system operations. In order to design the predictive controller effectively, we describe the computing procedure for each of the two important parameters. In addition, we introduce a projection matrix for determining the control input, which decreases the control performance function very rapidly. Finally, we depict various computer simulations on two representative chaotic systems (the Duffing and Hénon systems) so as to demonstrate the effectiveness of the new chaos control method.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E87-A No.5 pp.1270-1279

Publication Date: 2004/05/01

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Type of Manuscript: PAPER

Category: Neural Networks and Bioengineering

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Jong Tae CHOI, Yoon Ho CHOI, "Fuzzy Neural Network Based Predictive Control of Chaotic Nonlinear Systems" in IEICE TRANSACTIONS on Fundamentals, vol. E87-A, no. 5, pp. 1270-1279, May 2004, doi: .
Abstract: In this paper, we present a predictive control method, based on Fuzzy Neural Network (FNN), for the control of chaotic systems without precise mathematical models. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of the FNN are determined adaptively throughout system operations. In order to design the predictive controller effectively, we describe the computing procedure for each of the two important parameters. In addition, we introduce a projection matrix for determining the control input, which decreases the control performance function very rapidly. Finally, we depict various computer simulations on two representative chaotic systems (the Duffing and Hénon systems) so as to demonstrate the effectiveness of the new chaos control method.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/e87-a_5_1270/_p

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@ARTICLE{e87-a_5_1270,
author={Jong Tae CHOI, Yoon Ho CHOI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Fuzzy Neural Network Based Predictive Control of Chaotic Nonlinear Systems},
year={2004},
volume={E87-A},
number={5},
pages={1270-1279},
abstract={In this paper, we present a predictive control method, based on Fuzzy Neural Network (FNN), for the control of chaotic systems without precise mathematical models. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of the FNN are determined adaptively throughout system operations. In order to design the predictive controller effectively, we describe the computing procedure for each of the two important parameters. In addition, we introduce a projection matrix for determining the control input, which decreases the control performance function very rapidly. Finally, we depict various computer simulations on two representative chaotic systems (the Duffing and Hénon systems) so as to demonstrate the effectiveness of the new chaos control method.},
keywords={},
doi={},
ISSN={},
month={May},}

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TY - JOUR
TI - Fuzzy Neural Network Based Predictive Control of Chaotic Nonlinear Systems
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1270
EP - 1279
AU - Jong Tae CHOI
AU - Yoon Ho CHOI
PY - 2004
DO -
JO - IEICE TRANSACTIONS on Fundamentals
SN -
VL - E87-A
IS - 5
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - May 2004
AB - In this paper, we present a predictive control method, based on Fuzzy Neural Network (FNN), for the control of chaotic systems without precise mathematical models. In our design method, the parameters of both predictor and controller are tuned by a simple gradient descent scheme, and the weight parameters of the FNN are determined adaptively throughout system operations. In order to design the predictive controller effectively, we describe the computing procedure for each of the two important parameters. In addition, we introduce a projection matrix for determining the control input, which decreases the control performance function very rapidly. Finally, we depict various computer simulations on two representative chaotic systems (the Duffing and Hénon systems) so as to demonstrate the effectiveness of the new chaos control method.
ER -