Hybrid fuzzy convolution modelling and identification of chemical process systems

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11 Citations (Scopus)

Abstract

This paper looks at a new method of modelling nonlinear dynamic processes, using grid-type Takagi-Sugeno fuzzy models and a priori knowledge. The proposed hybrid fuzzy convolution dynamic model consists of a non-linear fuzzy steady-state static and a gainindependent impulse response model-based dynamic part. The modelling of nonlinear pH processes is chosen as a realistic case study for demonstration of the proposed modelling approach. The off-line identified hybrid fuzzy convolution model is shown to be capable of modelling the nonlinear process and providing better multiple-step prediction than the conventional grid-type Takagi-Sugeno fuzzy model.

Original languageEnglish
Pages (from-to)457-466
Number of pages10
JournalInternational Journal of Systems Science
Volume31
Issue number4
DOIs
Publication statusPublished - Jan 1 2000

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications

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