基于新扰动的BP神经网络控制混沌系统

2009 International Conference on Wavelet Analysis and Pattern Recognition Pub Date : 2009-07-12 DOI:10.1109/ICWAPR.2009.5207408

Xiaoping Zong, Jun Geng

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引用次数: 3

摘要

提出了一种新的扰动模型，并将其用于混沌系统的BP神经网络训练。该方法不需要预先了解待控制系统的相关知识，包括系统的维数和不稳定不动点的位置，可以推广到其他混沌控制中。在henon和Logistic映射上进行了实验，仿真结果表明该方法能使混沌呈现周期运动。

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Control chaotic systems based on BP neural network with a new perturbation

A new perturbation model is proposed, and used to train BP neural network for chaotic systems in this paper. The method requires no previous knowledge about the system to be controlled, including the dimensionality of the system and location of unstable fixed points, can be extended to other chaos control. It was tested on the henon and Logistic maps, and the simulation results showed that it could make the chaos present periodic motion.

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2009 International Conference on Wavelet Analysis and Pattern Recognition

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