Multimodel Decomposition of Nonlinear Dynamics Using Fuzzy Classification and Gap Metric Analysis

Gugaliya, Jinendra K.; Gudi, Ravindra D.

doi:10.3182/20100705-3-be-2011.00144

Cited by 2 publications

(1 citation statement)

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“…In Gugaliya and Gudi (2010) and Novak et al (2011) large number of clusters was initially assumed and later reduced by merging similar clusters. Teslic et al (2011) proposed an iterative incremental partitioning algorithm using G-K clustering.…”

Section: Introductionmentioning

confidence: 99%

A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems

Adeniran

El-Férik

2016

AIEDAM

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Several industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

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Section: Introductionmentioning

confidence: 99%