Abstract
A wavelet neural network with evolutionary programming is proposed in this paper. Unlike the conventional backpropagation training algorithm, the evolutionary programming does not require gradient information and can provide a stochastic optimal search. The proposed method is used to approximate nonlinear functions and solve classification problems. Some experimental results are proposed to show the potential of the evolving wavelet neural networks.
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