混沌光学系统的前向神经网络系统辨识研究

The Feedforward NN Identification of Chaotic Optical Systems

为解决混沌光学系统自适应控制所需之控制参考动力学模型问题，以布拉格声光混沌系统的系统辨识为例．研究了利用前向神经网络对混沌光学系统进行系统辩识的可行性。计算机仿真实验发现．在静态ＢＰ算法支持下一结构十分简单的三层ＢＰ前向神经网络（１：４：１）即可在一定的精度范围内完成对布拉格声光混沌系统的系统辨识．此结果表明．三层前向ＢＰ神经网络在静态ＢＰ算法的支持下确是一良好的混沌光学系统辨识器，因而可用来处理混沌光学时间序列以进行混沌光学系统的动力学重构。

in order to obtain the reference dynamic model for the adaptive control of thechaotic optical system, a suggestion of identifying the chaotic optical system with the BPfeed forward neural network supported by the BP algorithm is made in this peper. Thefeasibility of this suggestion was demonstrated with the ctrpputer simulation throughidentifying a Bragg acousto-optic bistable &. chaotic system with a very simple 1: 4: 1 BPNN. The result of the computer simulation shows that the three-layer BP foreward NN, iftrained with the BP algorithm, is indeed a fine identifier. Thus it could be used toreconstruct the dynamics of the chaotic optical system efficiently with its output time serieswith a satisfactory precision.