具有随机波动的脉冲神经网络的收敛稳定性

Chenhui Zhao, Shan He, Lin Li, Donghui Guo
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引用次数: 0

摘要

本文主要研究具有随机波动的尖峰神经网络的收敛稳定性问题。脉冲响应模型(SRM)的随机波动主要是由马尔可夫切换和时滞引起的。在该模型中,脉冲信号在神经元之间的传递应该是时间相关的,其核函数应该是Lipschitz连续的。利用m -矩阵的性质,给出了保证SRM稳定收敛的充分准则。稳定性结果对随机波动snn的优化计算和设计具有一定的参考价值。通过数值算例验证了所得结果的正确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Convergence Stability of Spiking Neural Networks with Stochastic Fluctuations
This paper is mainly concerned with the convergence stability of spiking neural networks (SNNs) with stochastic fluctuations. The stochastic fluctuations of spike response model (SRM) are mainly caused by Markovian switching and time delays. The transmission of pulse signals between neurons in this model should be time dependent and its kernel functions should be Lipschitz continuous. Some sufficient criteria are proposed to guarantee the stable convergence of the SRM by using the properties of M-matrix. The stability results have certain reference value for the optimal computation and the design of SNNs with stochastic fluctuations. The numerical illustration is provided to examine the validity of the derived results.
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