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Controlling chaos in a chaotic neural network.

Guoguang He1, Zhitong Cao, Ping Zhu

  • 1Department of Physics, Zhejiang University, 310028 Hangzhou, People's Republic of China. guoghe@mail.hz.zj.cn

Neural Networks : the Official Journal of the International Neural Network Society
|September 19, 2003
PubMed
Summary

This study introduces a pinning control method to stabilize chaotic neural networks. Computer simulations show this method effectively controls chaos, enabling networks to recall stored patterns by stabilizing their memory search process.

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Area of Science:

  • Computational neuroscience
  • Complex systems theory
  • Artificial neural networks

Background:

  • Chaotic neural networks exhibit associative memory but suffer from unstable memory search due to chaotic dynamics.
  • Stabilizing the network states is crucial for reliable associative memory retrieval.

Purpose of the Study:

  • To propose and validate a pinning control method for stabilizing chaotic neural networks.
  • To demonstrate the effectiveness of this control strategy in achieving stable associative memory recall.

Main Methods:

  • Development of a pinning control strategy specifically designed for chaotic neural networks.
  • Extensive computer simulations to analyze network behavior under control.
  • Investigation of the influence of control strength and pinning density on network stability.

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Main Results:

  • The proposed pinning control method successfully suppresses chaos in the neural network.
  • Network states converge to stored patterns when control strength and pinning density are appropriately selected.
  • Higher pinning density generally reduces the required control strength for stabilization.
  • Targeting variant neurons between initial and target patterns enhances control efficacy.

Conclusions:

  • Pinning control offers an effective approach to stabilize chaotic neural networks for reliable associative memory.
  • The findings provide insights into optimizing control parameters for enhanced network performance.
  • This method holds potential for improving the robustness of chaotic neural network-based memory systems.