Evolving fuzzy clustering algorithm based on maximum likelihood with participatory learning

This paper presents a fuzzy clustering algorithm based on maximum likelihood with participatory learning. The adopted methodology is based on an online fuzzy inference system with Takagi-Sugeno evolving structure, which employs an adaptive distance norm based on the maximum likelihood criterion with instrumental variable recursive parameter estimation. The performance and application of the proposed algorithm is based on the black box modeling of nonlinear system widely cited in the literature.