Generation of fuzzy rules based on fuzzy clustering and neuro-fuzzy methods

Based on the fuzzy clustering and neuro-fuzzy learning algorithms, we proposed a new technique for fuzzy rule generation. In this approach, before learning fuzzy rules we extract typical data from training data by using the fuzzy c-means clustering algorithm, in order to remove redundant data and resolve conflicts in data, and make them as practical training data. By these typical data, fuzzy rules can be tuned by using the neuro-fuzzy learning algorithm presented by authors [6-9]. Therefore, the learning time can be expected to be reduced and the fuzzy rules generated by the proposed approach are reasonable and suitable for the identified system model. Finally, identifying a nonlinear function also shows the efficiency of the presented method.