Novel Fuzzy System Identification: Comparative Study and Application for Data Forecasting

A novel fuzzy clustering technique for system identification based on the Takagi-Sugeno fuzzy inference is proposed. In this paper, the identification algorithm differs from others found in the literature, in the manner that the inference’s antecedents and consequents are built. Gaussian fuzzy sets with support on the cluster’s ordinal set of a determined α-level are unidimensional antecedents that have as a consequent an affine function, which recovers the attributes of the collected data. Two simulations examples are performed to compare to the new method: the comparisons are done with well-known algorithms in terms of the normalized root mean square goodness of the fit measure, and the computational speed in the identification process. For that purpose, Matlab System Identification toolbox is used in order to consolidate a unique source of comparison. The method has been tested in the prediction field, as part of a project supporting Brazilian local companies.