Please use this identifier to cite or link to this item:
|Title:||A noniterative neuro-fuzzy based identification method for Hammerstein processes||Authors:||Jia, L.
|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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jul 31, 2021
WEB OF SCIENCETM
checked on Jul 22, 2021
checked on Jul 9, 2021
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.