Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Identification of Dynamic Systems Using a Self Recurrent Wavelet Neural Network: Convergence Analysis Via Adaptive Learning Rates

자기 회귀 웨이블릿 신경 회로망을 이용한 다이나믹 시스템의 동정: 적응 학습률 기반 수렴성 분석

  • 유성진 (연세대학교 전기전자공학과) ;
  • 최윤호 (경기대학교 전기공학부) ;
  • 박진배 (연세대학교 전기전자공학과)
  • Published : 2005.09.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes an identification method using a self recurrent wavelet neural network (SRWNN) for dynamic systems. The architecture of the proposed SRWNN is a modified model of the wavelet neural network (WNN). But, unlike the WNN, since a mother wavelet layer of the SRWNN is composed of self-feedback neurons, the SRWNN has the ability to store the past information of the wavelet. Thus, in the proposed identification architecture, the SRWNN is used for identifying nonlinear dynamic systems. The gradient descent method with adaptive teaming rates (ALRs) is applied to 1.am the parameters of the SRWNN identifier (SRWNNI). The ALRs are derived from the discrete Lyapunov stability theorem, which are used to guarantee the convergence of an SRWNNI. Finally, through computer simulations, we demonstrate the effectiveness of the proposed SRWNNI.

Keywords

References

  1. K. S. Narendra and K. Parthasarthy, 'Identification and control of dynamical system using neural network,' IEEE Trans. Neural Networks, Vol. 1, No. 1, pp. 4-27, 1990 https://doi.org/10.1109/72.80202
  2. D. Rumalhart, G. E. Hinton, and R. J. Williams, 'Learning internal representations by error propagation,' in D. Rumelhart, and J. McClelland (Eds.), Parallel Distributed Processing, Cambrige, MA:MIT Press, vol. 1, pp. 318-362, 1986
  3. C. C. Ku, and K. Y. Lee, 'Diagonal recurrent neural networks for dynamics systems control,' IEEE Trans. on Neural Networks, vol. 6, no. 1, pp. 144-156, 1995 https://doi.org/10.1109/72.363441
  4. B. A. Pearlmutter, 'Learning state space trajectories in recurrent neural networks,' Proc. of Int. Joint Conference on Neural Networks, vol. 1, pp. 365-372, 1989 https://doi.org/10.1109/IJCNN.1989.118724
  5. R. J. Williams and D. Zipser, 'A learning algorithm for continually running recurrent neural network,' Neural Computing, vol. 1, no. 2, pp. 270-280, 1989 https://doi.org/10.1162/neco.1989.1.2.270
  6. S. H. Ling, H. K. Lam, F. H. F. Leung, and P. K. S. Tam, 'A novel GA-based neural network for short-term load forecasting,' Proc. of Int. Joint Conference on Neural Networks, vol. 3, pp. 2761-2766, 2002 https://doi.org/10.1109/IJCNN.2002.1007585
  7. S. Yao, C. Wei, and Z. He, 'Evolving wavelet neural networks for function approximation,' Electronics Letters, vol. 32, no. 4, pp. 360, 1996 https://doi.org/10.1049/el:19960229
  8. Q. Zhang and A. Benveniste, 'Wavelet networks,' IEEE Trans. Neural Networks, vol. 3, pp. 889-898, Nov. 1992 https://doi.org/10.1109/72.165591
  9. J. T. Choi, Y. H. Choi, and J. B. Park, 'Stable intelligent control of chaotic systems via wavelet neural network,' Proc. of Int. Conf. on Control, Automation and Systems, pp. 316-321, 2003
  10. J. Zhang, G. Walter, Y. Miao, and W. N. W. Lee, 'Wavelet neural networks for function learning,' IEEE Trans. on Signal Processing, vol. 43, no. 6, pp. 1485-1497, 1995 https://doi.org/10.1109/78.388860
  11. L. Cao, Y. Hong, H. Fang, and G. He, 'Predicting chaotic time series with wavelets networks,' Physica D, vol. 85, no. 1-2, pp. 225-238, 1995 https://doi.org/10.1016/0167-2789(95)00119-O
  12. Y. Oussar, I. Rivals, L. Personnaz, and G. Dreyfus, 'Training wavelet networks for nonlinear dynamic input-output modeling,' Neurocomputing, vol. 20, no. 1-3, pp. 173-188, 1998 https://doi.org/10.1016/S0925-2312(98)00010-1
  13. S. H. Strogatz, Nonlinear Dynamics and Chaos, Addison-Wesley, 1994