Online evolving fuzzy clustering algorithm based on maximum likelihood similarity distance

Abstract

This paper proposes an online evolving fuzzy clustering algorithm based on maximum likelihood estimator. In this algorithm, the distance from a point to center of the cluster is computed by maximum likelihood similarity of data. The mathematical formulation is developed from the Takagi–Sugeno (TS) fuzzy inference system. In order to evaluate the applicability of the proposed algorithm, the prediction of the Box- Jenkins (Gas Furnace) time series, is performed. Computational results of comparative analysis with other methods widely cited in the literature illustrates the effectiveness of the proposed algorithm.