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

Journal of control, automation and electrical systems

article

Torres, Luís Miguel Magalhães¹  de Oliveira Serra, Ginalber Luiz¹ 

[1] Federal Institute of Education, Sciences and Technology

关键词: Evolving fuzzy systems;    Multivariable dynamic systems;    State space;    System identification;   

DOI  :  10.1007/s40313-018-0393-8

学科分类：自动化工程

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY