Noise further expresses exponential decay for globally exponentially stable time-varying delayed neural networks

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• 作者：Song Zhu^a ; ^{songzhu82@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Qiqi Yang^a ; ^{yangqiqikiki@163.com" class="auth_mail" title="E-mail the corresponding author} ; Yi Shen^b ; ^{yishen64@163.com" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Delayed neural networks ; Exponential decay ; Noise ; Adjustable parameter
• 刊名：Neural Networks
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：77
• 期：Complete
• 页码：7-13
• 全文大小：415 K

文摘

This paper shows that the globally exponentially stable neural network with time-varying delay and bounded noises may converge faster than those without noise. And the influence of noise on global exponential stability of DNNs was analyzed quantitatively. By comparing the upper bounds of noise intensity with coefficients of global exponential stability, we could deduce that noise is able to further express exponential decay for DNNs. The upper bounds of noise intensity are characterized by solving transcendental equations containing adjustable parameters. In addition, a numerical example is provided to illustrate the theoretical result.