Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.jprocont.2005.03.006
Title: A noniterative neuro-fuzzy based identification method for Hammerstein processes
Authors: Jia, L. 
Chiu, M.-S. 
Ge, S.S. 
Keywords: Hammerstein model
Neuro-fuzzy system
Noniterative identification
Nonlinear processes
Issue Date: Oct-2005
Citation: Jia, L., Chiu, M.-S., Ge, S.S. (2005-10). A noniterative neuro-fuzzy based identification method for Hammerstein processes. Journal of Process Control 15 (7) : 749-761. ScholarBank@NUS Repository. https://doi.org/10.1016/j.jprocont.2005.03.006
Abstract: In this paper, a noniterative identification procedure for neuro-fuzzy based Hammerstein model is presented. The proposed method not only avoids 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 model. To construct the neuro-fuzzy based model, a clustering algorithm is presented to estimate the centers and widths of the model, and an analytical solution is developed to calculate the weights of the model in a noniterative manner. Examples are used to illustrate the applicability of the proposed method and a comparison with polynomial approach is made. © 2005 Elsevier Ltd. All rights reserved.
Source Title: Journal of Process Control
URI: http://scholarbank.nus.edu.sg/handle/10635/50847
ISSN: 09591524
DOI: 10.1016/j.jprocont.2005.03.006
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