Generalization ability analysis of one-dimensional wavelet neural network by simulations

In this paper, the generalization ability of one-dimensional wavelet neural network (I-DWNN) was discussed from four aspects which were training sample quality, network complexity, resembled over-fitting and extrapolation fitting. Simulations of the same problem with same training time showed that the over-fitting probability of the wavelet network was much bigger than multilayer perceptron (MLP) and radial basis function (RBF) network. Simulations of one problem with different network complexities showed that the network complexity had little impact on the generalization ability. Resembled over-fitting was discovered by simulations which debased the network generalization ability. To improve the network generalization ability, training method for high-noisy samples was discussed, wavelon-elimination algorithm dealing with resembled over-fitting, training method for the extrapolation fitting and other useful suggestions were proposed.