Skip to main content
Log in

Nonlinear dynamic system modeling using recurrent wavelet network

  • Published:
Journal of Electronics (China)

Abstract

A recurrent wavelet network for the dynamic system nonparametric modeling is proposed in this paper. It is noted that the suitable recurrent units are introduced so that the dynamics of the wavelet network has been greatly improved. The recurrent backpropagation identification algorithm is also given. The simulation results show that regress system model with large-dimension can be better constructed and the useful guidelines for initialization of the network parameter are also provided with recurrent wavelet network identification.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Q. Zhang, Using wavelet network in nonparametric estimation, IEEE Trans. on Neural Network, 8(1997)2, 227–236.

    Article  Google Scholar 

  2. K. Hornik, Multilayer feedforward networks are universal approximators, Neural networks, 2(1989), 359–366.

    Article  Google Scholar 

  3. A. Lemma et al., Multi-scale nonlinear system modeling using wavelet networks, SPIE Vol.2825, 1996, 600–605.

    Article  Google Scholar 

  4. W. Wei, A new recursive learning algorithm for recurrent neural network, ACTA Automatica Sinica, 24(1998)5, 616–621, (in Chinese).

    Google Scholar 

  5. C. M. Kuan, K. Hornik, H. Whit, A convergence results for learning in recurrent neural networks, Neural Computation, 6(1994)2, 420–440.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

Supported by Cao Guangpiao high-technology foundation

About this article

Cite this article

Wei, W. Nonlinear dynamic system modeling using recurrent wavelet network. J. of Electron.(China) 16, 193–199 (1999). https://doi.org/10.1007/s11767-999-0015-5

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11767-999-0015-5

Key words

Navigation