Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 2003.11c
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- Pages.421-424
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- 2003
Generalized Predictive Control of Chaotic Systems Using a Self-Recurrent Wavelet Neural Network
자기 회귀 웨이블릿 신경 회로망을 이용한 혼돈 시스템의 일반형 예측 제어
- Published : 2003.11.21
Abstract
This paper proposes the generalized predictive control(GPC) method of chaotic systems using a self-recurrent wavelet neural network(SRWNN). The reposed SRWNN, a modified model of a wavelet neural network(WNN), has the attractive ability such as dynamic attractor, information storage for later use. Unlike a WNN, since the SRWNN has the mother wavelet layer which is composed of self-feedback neurons, mother wavelet nodes of the SRWNN can store the past information of the network. Thus the SRWNN can be used as a good tool for predicting the dynamic property of nonlinear dynamic systems. In our method, the gradient-descent(GD) method is used to train the SRWNN structure. Finally, the effectiveness and feasibility of the SRWNN based GPC is demonstrated with applications to a chaotic system.
Keywords
- Self-recurrent wavelet neural network;
- Generalized predictive control;
- Gradient descent;
- Chaotic system;
- Chaos control