In this paper a new fuzzy modeling approach is proposed, which is devoted to discover knowledge from data and represent it in the form of fuzzy rules. In this approach, the structure identification and parameter optimization are carried out automatically without any assumption about the structure of the data, which is capable of finding the optimal number of the rules with an acceptable accuracy. The proposed fuzzy modeling approach has three significantmodules: (1) Generate initial rule-base, (2) Construct a new rule and add to rule-base, (3) Tune rule-base. The proposed approach has been successfully applied to benchmark data sets. The results show the superiority of the model in comparison with the other fuzzy models in terms of error reduction and simplicity.

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