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A hybrid algorithm for structure identification of neuro-fuzzy modeling

The problems with which we are often confronted in neuro-fuzzy modeling are how to adequately decide the number of fuzzy rules extracted from a set of input-output data and how to precisely define the membership functions of each fuzzy rule. In this paper, we propose a hybrid algorithm that can automatically extract fuzzy rules from a set of numerical data points. Our algorithm is mainly composed of two phases, viz. data partitioning and rule extraction. In the first phase, the data set is partitioned into several clusters according to the similarities between the data points. In other words, the nearby data points are grouped into the same cluster. This is completed by a sequence of combinations of movable fuzzy prototypes. In the second phase, a fuzzy IF-THEN rule is extracted from each cluster and the membership functions of the corresponding rule are determined by statistical techniques. Experimental results show that the proposed algorithm converges quickly and can generate fewer rules with a lower mean-square error.