Abstract
In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.
| Original language | English |
|---|---|
| Pages (from-to) | 43-55 |
| Number of pages | 13 |
| Journal | International Journal of Control, Automation and Systems |
| Volume | 3 |
| Issue number | 1 |
| Publication status | Published - 2005 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
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Stable predictive control of chaotic systems using self-recurrent wavelet neural network. / Yoo, Sung Jin; Park, Jin Bae; Choi, Yoon Ho.
In: International Journal of Control, Automation and Systems, Vol. 3, No. 1, 01.03.2005, p. 43-55.Research output: Contribution to journal › Article
TY - JOUR
T1 - Stable predictive control of chaotic systems using self-recurrent wavelet neural network
AU - Yoo, Sung Jin
AU - Park, Jin Bae
AU - Choi, Yoon Ho
PY - 2005/3/1
Y1 - 2005/3/1
N2 - In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.
AB - In this paper, a predictive control method using self-recurrent wavelet neural network (SRWNN) is proposed for chaotic systems. Since the SRWNN has a self-recurrent mother wavelet layer, it can well attract the complex nonlinear system though the SRWNN has less mother wavelet nodes than the wavelet neural network (WNN). Thus, the SRWNN is used as a model predictor for predicting the dynamic property of chaotic systems. The gradient descent method with the adaptive learning rates is applied to train the parameters of the SRWNN based predictor and controller. The adaptive learning rates are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of the predictive controller. Finally, the chaotic systems are provided to demonstrate the effectiveness of the proposed control strategy.
UR - http://www.scopus.com/inward/record.url?scp=14844363153&partnerID=8YFLogxK
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M3 - Article
AN - SCOPUS:14844363153
VL - 3
SP - 43
EP - 55
JO - International Journal of Control, Automation and Systems
JF - International Journal of Control, Automation and Systems
SN - 1598-6446
IS - 1
ER -