Recurrent Wavelet Neural Network Based on Immune Evolving Algorithm and its Application in Nonlinear Modeling

Xiang Jie Luo; Cheng Kai Wei; Hai Long Gao

doi:10.4028/www.scientific.net/AMR.1049-1050.1666

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 1049-1050Recurrent Wavelet Neural Network Based on Immune...

Recurrent Wavelet Neural Network Based on Immune Evolving Algorithm and its Application in Nonlinear Modeling

Abstract:

To improve the modeling performance of Recurrent Wavelet Neural Network (RWNN), a training algorithm based on Immune Evolving Algorithm (IEA) is proposed. In the process of RWNN training, IEA is mainly used to optimize the connection weight, translating and scaling parameter. The experiment result on Duffing chaotic time series shows that the proposed RWNN training algorithm has a good prediction capability in the field of nonlinear modeling.

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Periodical:

Advanced Materials Research (Volumes 1049-1050)

Pages:

1666-1669

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1666

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Online since:

October 2014

Authors:

Xiang Jie Luo*, Cheng Kai Wei, Hai Long Gao

Keywords:

Chaotic Time Series , Immune Evolving Algorithm, Nonlinear Modeling, Recurrent Wavelet Neural Network

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* - Corresponding Author

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