期刊
NONLINEAR DYNAMICS
卷 81, 期 3, 页码 1081-1095出版社
SPRINGER
DOI: 10.1007/s11071-015-2050-0
关键词
Identification; Chaotic system; Hammerstein model; Extreme learning machine; Generalized ELM algorithm
资金
- National Natural Science Foundation of China [61273260]
- Natural Science Foundation of Hebei Province [F201420-3208]
- Specialized Research Fund for the Doctoral Program of Higher Education of China [20121333120010]
- China Postdoctoral Science Foundation [2013M530888]
- Special Grant of the China Postdoctoral Science Foundation [2014T70229]
- Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, P. R. China [SCIP2012008]
In this paper, a new method is proposed to identify chaotic system based on Hammerstein-extreme learning machine (Hammerstin-ELM) model. Hammerstein-ELM model consists of a static nonlinear function followed by a linear dynamic part. The static nonlinear function is represented by a ELM neural network. A generalized ELM algorithm is presented to simultaneously identify the parameters of linear dynamic part and those of ELM neural networks. Numerical examples demonstrate the effectiveness of the proposed method.
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