State-Space Recursive Fuzzy Modeling Approach Based on Evolving Data Clustering
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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.
KeywordsEvolving fuzzy systems Multivariable dynamic systems State space System identification
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