基于平稳小波和相空间重构的激光混沌预测

提出了一种激光混沌时间序列预测算法.该算法通过平稳小波分解，将原始数据序列分解为与原序列等长的尺度系数和小波系数，利用坐标延迟理论，重建各级尺度系数和各级小波系数的相空间,再根据混沌吸引子的稳定性和分形性,在相空间中对尺度系数和小波系数进行预测,进而通过平稳小波重构算法,实现了时间序列的非线性预测.该算法对数据可以进行更平滑的处理，比无小波算法预测的时间范围更长.通过仿真试验说明，原始时间数据序列被成功的重建，说明算法能够有效的对非线性动态系统的时间序列进行建模和预测.

Prediction Algorithm for Laser Chaotic Based on Stationary Wavelet Transform and Reconstructed Phase Space

A new algorithms for laser chaotic time series prediction was presented. The origin time series was decomposed as the measurement coefficients and wavelet coefficients based on the discrete stationary wavelet transform algorithms. The phase space of these coefficients was reconstructed by the theory of time delays. Based on the stability and the fractal of the chaotic attractor, the coefficients were predicted in their phase space. Finally the prediction of the coefficients were acquired by the inverse discrete stationary wavelet transform . The proposed algorithm was a better candidate for long range prediction. The simulation results prove the similarity of dynamic invariants between the origin and generated time series. And it shows that the proposed method can capture the dynamics of the nonlinear systems series effectively.