대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)
- 대한전기학회 2004년도 하계학술대회 논문집 D
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- Pages.2209-2211
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- 2004
자기 회귀 웨이블릿 신경 회로망을 이용한 비선형 혼돈 시계열의 예측에 관한 연구
A Study on the Prediction of the Nonlinear Chaotic Time Series Using a Self-Recurrent Wavelet Neural Network
- Lee, Hye-Jin (Dept. of Electrical and Electronic Eng. Yonsei Univ.) ;
- Park, Jin-Bae (Dept. of Electrical and Electronic Eng. Yonsei Univ.) ;
- Choi, Yoon-Ho (School of Electronic Eng. Kyonggi Univ.)
- 발행 : 2004.07.14
초록
Unlike the wavelet neural network, since a mother wavelet layer of the self-recurrent wavelet neural network (SRWNN) is composed of self-feedback neurons, it has the ability to store past information of the wavelet. Therefore we propose the prediction method for the nonlinear chaotic time series model using a SRWNN. The SRWNN model is learned for the modeling of a function such that the inputs arc known values of the time series and the output is the value in the future. The parameters of the network are tuned to minimize the difference between the nonlinear mapping of the chaotic time series and the output of SRWNN using the gradient-descent method for the adaptive backpropagation algorithm. Through the computer simulations, we demonstrate the feasibility and the effectiveness of our method for the prediction of the logistic map and the Mackey-Glass delay-differential equation as a nonlinear chaotic time series.
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