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논문 상세정보

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.

저자의 다른 논문

참고문헌 (16)

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이 논문을 인용한 문헌 (6)

  1. Hyun, Keun-Ho ; Ka, Chool-Hyun 2005. "Design of a Robust Adaptive Backstepping Controller for a Chaos System with Disturbances" 전기학회논문지. The Transactions of the Korean Institute of Electrical Engineers. P, 54(3): 119~128 
  2. Yoo, Sung-Jin ; Choi, Yoon-Ho ; Park, Jin-Bae 2006. "Robust Flight Control System Using Neural Networks: Dynamic Surface Design Approach" 전기학회논문지. The transactions of the Korean Institute of Electrical Engineers. D / D, 시스템 및 제어부문, 55(12): 518~525 
  3. Yoo Sung-Jin ; Park Jin-Bae 2006. "Adaptive Backstepping Control Using Self Recurrent Wavelet Neural Network for Stable Walking of the Biped Robots" 제어·자동화·시스템공학 논문지 = Journal of control, automation and systems engineering, 12(3): 233~240 
  4. Lee, Sin-Ho ; Choi, Yoon-Ho ; Park, Jin-Bae 2007. "Terminal Sliding Mode Control of Nonlinear Systems Using Self-Recurrent Wavelet Neural Network" 제어·자동화·시스템공학 논문지 = Journal of control, automation and systems engineering, 13(11): 1033~1039 
  5. 2007. "" International Journal of Control, Automation and Systems, 5(3): 269~282 
  6. 2014. "" International Journal of Control, Automation and Systems, 12(1): 177~187 

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