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Neural network system identification of chaotic optical systems with the chaos speedup BP algorithm

机译:混沌加速BP算法在混沌光学系统神经网络识别中的应用。

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Abstract: An algorithm for quickly training the BP neural network system identifier (BPNNSI) of the chaotic optical systems is presented in this paper. The ability of this algorithm, termed as the chaos speedup BP algorithm (CSBPA), has been demonstrated with the computer simulation of identifying the Bragg diffraction acousto-optic system (BDAOS) in which a 1:4:1 BP network was employed in identification. Taking the normalized output time series of the BDAOS as the training series, the BPNNSI was trained with the CSBPA as follows: (1) trained the BPNNSI to learn a chaotic state of the BDAOS with the BP algorithm where the initial weight distribution was set randomly; (2) took the final weight distribution obtained in (1) as the initial weight distribution for the other states of the BDAOS to be identified; (3) trained the BPNNSI to learn the other states still with the BP algorithm but with the initial weight distribution obtained in (2).!10
机译:摘要:本文提出了一种快速训练混沌光学系统的BP神经网络系统标识符(BPNNSI)的算法。该算法称为混沌加速BP算法(CSBPA)的能力已经通过识别BRAGG衍射声光学系统(BDAOS)的计算机模拟,其中在识别中使用1:4:1BP网络。采用BDAOS的标准化输出时间序列作为培训系列,BPNNSI用CSBPA培训,如下:(1)培训了BPNNSI,以便在随机设置初始重量分布的BP算法中学习BDAOS的混沌状态; (2)在(1)中获得的最终重量分布作为待定BDAOS的其他国家的初始重量分布; (3)培训了BPNNSI仍然使用BP算法学习其他州,但在(2)中获得的初始重量分布。!10

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