混沌光学系统之前向神经网络混沌加速的系统辨识研究

Chaos Speedup Feedforward Neural Network Identification of Chaotic Optical Systems

研究了利用前向神经网络对混沌光学系统进行混沌加速系统辨识的可能性。计算机数值仿真发现，利用三层前向神经网络混沌光学系统辨识器，在基于混沌动力学角度的修正ＢＰ算法（混沌加速ＢＰ算法）支持下可克服由常规ＢＰ算法导致的辨识时间长的缺点，在较少的训练次数内即可对布拉格声光双稳混沌系统进行良好的系统辨识。此研究结果表明，在混沌加速ＢＰ算法的支持下，三层前向神经网络可用来快速处理混沌光学时间序列以进行相应的动力学重构。

The identification of the chaotic optical system with BP feedforward neural network supported by a modified BP algorithm is suggested. The feasibility of this suggestion was demonstrated with computer simulation of the identification of the Bragg acoustooptic bistable & chaotic system. The computer simulation result shows that, the three layers foreward neural network, if trained with the chaos speedup BP algorithm, is indeed a fine identifier with less training iterations than usual. Thus it could be used to reconstruct the dynamics of the chaotic optical system with its output time series very efficiently.