Prediction of the Chaotic Time Series Using Adaptive Orthogonal Wavelet Neural Network
Junhua Liu · Journal of Xi'an University of Technology · 2008
With an aim at the prediction problem of nonlinear chaotic time series,a novel adaptive predictive algorithm based on orthogonal wavelet neural network is suggested.Based on the desired input-output data coming from the nonlinear series models,the initial wavelet neural network is established using the wavelet framework theory.The orthogonal stepwise selection method is adopted to optimize the initial wavelet neural network,whereby the simplified network model is established.And at the same time,the on-line learning algorithm,on-line revised network weight values and the parameters of wavelet neural elements are introduced to improve the adaptability and pan-capability.The algorithm effectiveness is proved via the prediction of the time delay Mackey-Glass overtime series and time variation Lorenz chaotic series.