Prediction of chaotic time series based on wavelet neural network

Wavelet neural network possesses the best function approximation ability, that is to say it has the ability to identify the model. Because the constricting model algorithm is different from common artificial neural network BP algorithm, it can effectively overcome intrinsic defect of common artificial neural network. Therefore the better prediction effect can be reached effectively. The paper gives a method of prediction model of chaotic time series based on wavelet neural network that enables prediction model to have not only wavelet good approximation property, but also neural network self-learning adaptive quality. The authors make use the method to predict sea clutter data