Non uniform noisy data training using Wavelet neural network based on sampling theory

摘要

Global convergence and overfitting are the main problem in neural network training. One of the new methods to overcome these problems is sampling theory that is applied in training of wavelet neural network. In this paper this new method is improved for training of wavelet neural network in non uniform and noisy data. The improvements include suggesting a method for finding the appropriate feedback matrix, addition of early stopping and wavelet thresholding to training procedure. Two experiments are conducted for one and two dimensional function. The results establish a satisfied performance of this algorithm in reduction of generalization error, reduction the complexity of wavelet neural network and mainly avoiding overfitting.