Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/50530
Title: An extended self-organizing map for nonlinear system identification
Authors: Ge, M.
Chiu, M.-S. 
Wang, Q.-G. 
Issue Date: Oct-2000
Source: Ge, M.,Chiu, M.-S.,Wang, Q.-G. (2000-10). An extended self-organizing map for nonlinear system identification. Industrial and Engineering Chemistry Research 39 (10) : 3778-3788. ScholarBank@NUS Repository.
Abstract: Local model networks (LMN) are recently proposed for modeling a nonlinear dynamical system with a set of locally valid submodels across the operating space. Despite the recent advances of LMN, a priori knowledge of the processes has to be exploited for the determination of the LMN structure and the weighting functions. However, in most practical cases, a priori knowledge may not be readily accessible for the construction of LMN. In this paper, an extended self-organizing map (ESOM) network, which can overcome the aforementioned difficulties, is developed to construct the LMN. The ESOM is a multilayered network that integrates the basic elements of a traditional self-organizing map and a feed-forward network into a connectionist structure. A two-phase learning algorithm is introduced for constructing the ESOM from the plant input-output data, with which the structure is determined through the self-organizing phase and the model parameters are obtained by the linear least-squares optimization method. Literature examples are used to demonstrate the effectiveness of the proposed scheme.
Source Title: Industrial and Engineering Chemistry Research
URI: http://scholarbank.nus.edu.sg/handle/10635/50530
ISSN: 08885885
Appears in Collections:Staff Publications

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