State-Space Recursive Fuzzy Modeling Approach Based on Evolving Data Clustering

In this paper, an online evolving fuzzy Takagi–Sugeno state-space model identification approach for multivariable dynamic systems is proposed. The proposed methodology presents an evolving fuzzy clustering algorithm based on the concept of recursive density estimation for online antecedent structure...

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Bibliographic Details
Published inJournal of control, automation & electrical systems Vol. 29; no. 4; pp. 426 - 440
Main Authors Torres, Luís Miguel Magalhães, de Oliveira Serra, Ginalber Luiz
Format Journal Article
LanguageEnglish
Published New York Springer US 01.08.2018
Springer Nature B.V
Subjects
Algorithms
Clustering
Control
Control and Systems Theory
Electrical Engineering
Engineering
Evolution
Fuzzy systems
Markov chains
Mechatronics
Modelling
Recursive methods
Robotics
Robotics and Automation
State space models
State space
System identification
Evolving fuzzy systems
Multivariable dynamic systems
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Summary:In this paper, an online evolving fuzzy Takagi–Sugeno state-space model identification approach for multivariable dynamic systems is proposed. The proposed methodology presents an evolving fuzzy clustering algorithm based on the concept of recursive density estimation for online antecedent structure adaptation according to the data. For estimation of the minimum realization state-space models in the consequent of the fuzzy rules is proposed a recursive methodology based on the eigensystem realization fuzzy algorithm using the system fuzzy Markov parameters obtained recursively from experimental data. Experimental results from the modeling of multivariable nonlinear evaporator process are presented.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-018-0393-8