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Chaos control of ferroresonance system based on improved RBF neural network

Sima Wen-Xia Liu Fan Sun Cai-Xin Liao Rui-Jin Yang Qing

Chaos control of ferroresonance system based on improved RBF neural network

Sima Wen-Xia, Liu Fan, Sun Cai-Xin, Liao Rui-Jin, Yang Qing
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  • Abstract views:  3540
  • PDF Downloads:  1037
  • Cited By: 0
Publishing process
  • Received Date:  01 July 2005
  • Accepted Date:  04 June 2006
  • Published Online:  20 November 2006

Chaos control of ferroresonance system based on improved RBF neural network

  • 1. 重庆大学高电压与电工新技术教育部重点实验室,重庆 400044

Abstract: Facing to the ferroresonance over voltage of neutral grounded power system, an improved learning algorithm based on RBF neural networks is used to control the chaos system. The algorithm optimizes the object function to derive learning rule of central vectors, and uses the clustering function of network hidden layers.It improves the regression and learning ability of neural networks. The academic derivation testifies the errors and precision could satisfy demand of chaos control.And simulation calculation also displayed that the rate of convergence of the improved RBF neural network is much quickly and approach ability is much stronger. The numerical experimentation of ferroresonance system testifies the reliability and stability of using the algorithm to control chaos in neutral grounded power system.

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