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Noise suppress exponential growth for hybrid Hopfield neural networks

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  • Zhu, Song
  • Shen, Yi
  • Chen, Guici

Abstract

In this paper, we show that noise can transform a hybrid neural networks, whose solution may grow exponentially, into a new stochastic one, whose solution grows at most polynomially. In other words, we reveal that noise can suppress the exponential growth in hybrid Hopfield neural networks.

Suggested Citation

  • Zhu, Song & Shen, Yi & Chen, Guici, 2012. "Noise suppress exponential growth for hybrid Hopfield neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 86(C), pages 10-20.
    • Handle: RePEc:eee:matcom:v:86:y:2012:i:c:p:10-20
    DOI: 10.1016/j.matcom.2010.11.014
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    References listed on IDEAS

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    1. Huang, Chuangxia & Huang, Lihong & Yuan, Zhaohui, 2005. "Global stability analysis of a class of delayed cellular neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 70(3), pages 133-148.
    2. Song, Qiankun & Cao, Jinde, 2007. "Global robust stability of interval neural networks with multiple time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 74(1), pages 38-46.
    3. Sun, Changyin & Feng, Chun-Bo, 2004. "Exponential periodicity and stability of delayed neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 66(6), pages 469-478.
    4. Mao, Xuerong & Marion, Glenn & Renshaw, Eric, 2002. "Environmental Brownian noise suppresses explosions in population dynamics," Stochastic Processes and their Applications, Elsevier, vol. 97(1), pages 95-110, January.
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