Abstract：The three-stage strategy，proposed by Murtagh，for complicated time series prediction with wavelet and neural networks is improved. Based on the improved strategy，a new prediction model of decoupled wavelet and optimal neural network is proposed to increase the ability of neural networks for dam deformation prediction. Firstly，a new space reconstruction technique based on the main peak reconstruction of energy spectrum of original data is developed using the quasi-wavelet-packet property of redundant Haar wavelet；and then，it is used to substitute for the first stage of the three-stage strategy to built a better neural networks operating platform. Secondly，the optimal-refreshing window technique for neural network self-optimization is developed by using the optimal brain surgeon technique to prune the networks coefficients，and the inner condition of the neural network is optimized by substituting it for the second stage of the three-stage strategy. The adaptability and the analyzing ability of the newly reformed model are increased for the prediction of complicated dynamic system．It is proved by many evaluating indices such as relative mean square error，cross-relation，normal mean square error and direction symmetry in a case of predicting dam deformation that its function is significantly improved as compared with that of the three-stage strategy.
