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|Title:||An extended self-organizing map for nonlinear system identification|
|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|
|Appears in Collections:||Staff Publications|
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