An extended self-organizing map for nonlinear system identification

M. Ge, M. S. Chiu, Q. G. Wang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)3778-3788
Number of pages11
JournalIndustrial & Engineering Chemistry Research
Issue number10
Publication statusPublished - 2000
Externally publishedYes

ASJC Scopus subject areas

  • General Chemistry
  • General Chemical Engineering
  • Industrial and Manufacturing Engineering


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