MIMO evolving learning based on maximum likelihood algorithm applied to black box fuzzy modeling for systems identification design

摘要

This paper presents an overview of a specific application to computational intelligence techniques, specifically, evolving fuzzy systems: online fuzzy inference system with Takagi-Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion online with instrumental variable recursive parameter estimation. The performance and application of the proposed methodology is based on the black box modeling.