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

descriptionPublicationkeyboard_double_arrow_right Article 01 Oct 2014Publisher:Trans Tech Publications, Ltd.Journal:Advanced Materials Research, volume 1049-1050, pages 1,666-1,669 (eissn: 1662-8985,

Authors: Xiang Jie Luo; Cheng Kai Wei; Hai Long Gao;

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

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

- Summary
- Metrics

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.

Related Organizations

Air Force Engineering University
China (People's Republic of)

Impact byBIP!

	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	0
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Average