Please use this identifier to cite or link to this item:
DC FieldValue
dc.titleNeuro-fuzzy system based identification method for hammerstein processes
dc.contributor.authorJia, L.
dc.contributor.authorChiu, M.-S.
dc.contributor.authorGe, S.S.
dc.identifier.citationJia, L.,Chiu, M.-S.,Ge, S.S. (2004). Neuro-fuzzy system based identification method for hammerstein processes. 2004 5th Asian Control Conference 1 : 104-111. ScholarBank@NUS Repository.
dc.description.abstractHammerstein model, which consists of the cascade structure of a static nonlinearity followed by a linear dynamic part, can effectively describe the nonlinear dynamics of many industrial processes. To circumvent the open problems in existing identification methods of Hammerstein processes, Sung developed a new system identification method, which completely separates the identification problem of the linear dynamic part from that of nonlinear static part using a special test signal. However, the polynomials are employed to approximate the nonlinear static function with some conditions that may limit its practical applications. To alleviate this problem, neuro-fuzzy system is employed in this paper to describe the nonlinear static function of the Hammerstein model without any prior knowledge and restriction on static nonlinear function. Further-more, a non-iterative algorithm is proposed to obtain the neuro-fuzzy system based nonlinear static model. Literature examples are used to illustrate the performance and applicability of the proposed Hammerstein model.
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.sourcetitle2004 5th Asian Control Conference
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Page view(s)

checked on Nov 24, 2022

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.