Mean-square exponential input-to-state stability for neutral stochastic neural networks with mixed delays

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• 作者：Yinfang Song^a ; ^{songyinfang2006@sina.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Wen Sun^a ; ^{sunw@amss.ac.cn" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Feng Jiang^b ; ^{fjiang78@163.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Neutral stochastic neural networks ; Mixed delays ; Mean-square exponential input-to-state stability ; It o^׳s formula ; Lyapunov&ndash ; Krasovskii functional
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：12 September 2016
• 年：2016
• 卷：205
• 期：Complete
• 页码：195-203
• 全文大小：665 K

文摘

This paper is concerned with the input-to-state stability problem of a class of neutral stochastic neural networks. The stochastic neural networks that we consider contain both neutral terms and mixed delays. By utilizing the Lyapunov–Krasovskii functional method, stochastic analysis techniques and It class="mathmlsrc">title="View the MathML source" class="mathImg" data-mathURL="/science?_ob=MathURL&_method=retrieve&_eid=1-s2.0-S0925231216302557&_mathId=si0002.gif&_user=111111111&_pii=S0925231216302557&_rdoc=1&_issn=09252312&md5=666d4c2e8bd4068d90c5283160b076de">class="imgLazyJSB inlineImage" height="11" width="9" alt="View the MathML source" style="margin-top: -5px; vertical-align: middle" title="View the MathML source" src="/sd/grey_pxl.gif" data-inlimgeid="1-s2.0-S0925231216302557-si0002.gif">class="mathContainer hidden">class="mathCode">$\widehat{\mathrm{o}}$׳s formula, some sufficient conditions are derived to ensure the mean-square exponential input-to-state stability of the addressed system. Two numerical examples and their simulations are given to illustrate the effectiveness of the derived results.