Nonlinear function approximation is one of the most important tasks in system analysis and identification. Several models have been presented to achieve an accurate approximation on nonlinear mathematics functions. However the majority of the models are specific to certain problems and systems. In this paper, an evolutionary-based wavelet neural network model is proposed for structure definition and optimization of nonlinear systems. The proposed model involves structure identification and also a parameter tuning phase to be adapted for modeling of an arbitrary system. The proposed structure and the learning algorithm arevalidated by comparing with some other most commonly used alternatives. The simulation shows the performance and adaptability of the proposed model in approximating multivariate nonlinear mathematics functions.

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