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One of the main problems for effective control of an uncertain system is the creation of the proper knowledge base for the control system. In this paper, the integration of fuzzy set theory and wavelet neural networks (WNNs) is proposed to alleviate the problem. The proposed fuzzy WNN is constructed on the base of a set of fuzzy rules. Each rule includes a wavelet function in the consequent part of the rule. The parameter update rules of the system are derived based on the gradient descent method. The structure is tested for the identification and the control of the dynamic plants commonly used in the literature. It is seen that the proposed structure results in a better performance despite its smaller parameter space.