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首页> 外文期刊>Mathematical Problems in Engineering >Preserving Global Exponential Stability of Hybrid BAM Neural Networks with Reaction Diffusion Terms in the Presence of Stochastic Noise and Connection Weight Matrices Uncertainty
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Preserving Global Exponential Stability of Hybrid BAM Neural Networks with Reaction Diffusion Terms in the Presence of Stochastic Noise and Connection Weight Matrices Uncertainty

机译：在随机噪声和连接权矩阵不确定的情况下，保留具有反应扩散项的混合BAM神经网络的全局指数稳定性

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摘要

We study the impact of stochastic noise and connection weight matrices uncertainty on global exponential stability of hybrid BAM neural networks with reaction diffusion terms. Given globally exponentially stable hybrid BAM neural networks with reaction diffusion terms, the question to be addressed here is how much stochastic noise and connection weights matrices uncertainty the neural networks can tolerate while maintaining global exponential stability. The upper threshold of stochastic noise and connection weights matrices uncertainty is defined by using the transcendental equations. We find that the perturbed hybrid BAM neural networks with reaction diffusion terms preserve global exponential stability if the intensity of both stochastic noise and connection weights matrices uncertainty is smaller than the defined upper threshold. A numerical example is also provided to illustrate the theoretical conclusion.
机译：我们研究了随机噪声和连接权重矩阵的不确定性对具有反应扩散项的混合BAM神经网络的全局指数稳定性的影响。给定具有反应扩散项的全局指数稳定的混合BAM神经网络，此处要解决的问题是在维持全局指数稳定性的同时，神经网络可以容忍多少随机噪声和连接权重矩阵不确定性。随机噪声和连接权重矩阵不确定性的上限通过使用先验方程定义。我们发现，如果随机噪声和连接权矩阵不确定性的强度都小于定义的上限，则具有反应扩散项的扰动混合BAM神经网络将保持全局指数稳定性。还提供了一个数值示例来说明理论结论。

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  • 来源
    《Mathematical Problems in Engineering》 |2014年第8期|486052.1-486052.17|共17页
  • 作者

    Yan Li; Yi Shen;

  • 作者单位

    Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China,College of Science, Huazhong Agriculture University, Wuhan 430079, China;

    Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;

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