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Combined separable signals based neuro-fuzzy Hammerstein–Wiener model

Content Provider	Springer Nature Link
Author	Li, Jia Qiliang, Feng
Copyright Year	2016
Abstract	Hammerstein–Wiener model can describe a large number of complicated industrial processes. In this paper, a novel identification method for neuro-fuzzy based Hammerstein–Wiener model is presented. A neuro-fuzzy system with correlation analysis based non-iterative parameter updating algorithm is proposed to model the static nonlinearity of Hammerstein–Winer processes. As a result, the proposed method not only avoid the inevitable restrictions on static nonlinear function encountered by using the polynomial approach, but also overcomes the problems of initialization and convergence of the model parameters, which are usually resorted to trial and error procedure in the existing iterative algorithms used for the identification of Hammerstein–Winer model. In addition, combined separable signals are adopted to identify the Hammerstein–Wiener process, resulting in the identification problem of the linear model separated from that of nonlinear parts. Moreover, one part of the input signals is extended to more general signals, such as binary signals, Gaussian signals or other modulated signals. Examples are used to illustrate the effectiveness of the proposed method.
Starting Page	245
Ending Page	259
Page Count	15
File Format	PDF
ISSN	18659284
Journal	Memetic Computing
Volume Number	9
Issue Number	3
e-ISSN	18659292
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2016-08-18
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Hammerstein–Wiener model Combined separable signals Independent separable signals Uniformly random multi-step signals The correlation algorithm Neuro-fuzzy based identification method ApplicationMathematics/Computational Methods of Engineering Artificial Intelligence (incl. Robotics) Complex Systems Control, Robotics, Mechatronics Bioinformatics Applications of Mathematics
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Computer Science

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