In this paper, an efficient method is proposed to design fuzzy model with wavelet transforms for function learning. The structure is based on the basis of fuzzy rules including wavelet functions in the consequent parts of rules. In order to improve the function approximation accuracy and general capability of the system, an efficient genetic algorithm (GA) approach is used to adjust the parameters of dilation, translation, weights, and membership functions. By minimizing a quadratic measure of the error derived from the output of the system, the design problem can be characterized by the proposed GA formulation. The performance of our approximation is superior to that of the existing methods. Also, one numerical design example is presented to demonstrate the design flexibility and usefulness of this presented approach.
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