{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T16:08:26Z","timestamp":1784218106954,"version":"3.55.0"},"reference-count":17,"publisher":"European Society of Computational Methods in Sciences and Engineering","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"published-print":{"date-parts":[[2023,4,4]]},"abstract":"<jats:p>This paper studies stochastic asymptotic stability for stochastic inertial Cohen-Grossberg neural networks with time-varying delay. Firstly, the second-order differential equation is converted into the first-order differential equation by appropriate variable substitution. Secondly, the existence of the equilibrium point is derived by using homeomorphic mapping, finite increment formula of Lagrange mean value theorem and linear matrix inequality. The sufficient conditions for the stochastic asymptotic stability of the equilibrium point of the system are derived by defining the appropriate operator, and constructing the appropriate positive Lyapunov function and positive-definite matrix. Thirdly, a numerical example illustrates the correctness of these theorems.<\/jats:p>","DOI":"10.3233\/jcm-226480","type":"journal-article","created":{"date-parts":[[2022,11,11]],"date-time":"2022-11-11T11:41:25Z","timestamp":1668166885000},"page":"921-931","source":"Crossref","is-referenced-by-count":3,"title":["Stochastic asymptotic stability for stochastic inertial Cohen-Grossberg neural networks with time-varying delay"],"prefix":"10.66113","volume":"23","author":[{"given":"Danning","family":"Xu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"55691","reference":[{"key":"10.3233\/JCM-226480_ref1","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.neucom.2016.09.038","article-title":"Mean-square exponential input-to-state stability of stochastic recurrent neural networks with multi-proportionaldelays","volume":"219","author":"Zhou","year":"2017","journal-title":"Neurocomputing."},{"issue":"1","key":"10.3233\/JCM-226480_ref2","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1080\/21642583.2018.1544512","article-title":"A new result on the mean-square exponential input-to-state stability of stochastic delayed recurrent neural networks","volume":"6","author":"Wang","year":"2018","journal-title":"Systems Science & Control Engineering."},{"issue":"4","key":"10.3233\/JCM-226480_ref3","first-page":"731","article-title":"Mean-square exponential input state stability of stochastic fuzzy Cohen Grossberg neural networks with time-varying delays","volume":"53","author":"Zhou","year":"2016","journal-title":"Journal of Sichuan University (Natural Science Edition)."},{"key":"10.3233\/JCM-226480_ref4","doi-asserted-by":"crossref","unstructured":"Chen S, Feng JW, Wang JY, Zhao Y. 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Mean-square exponential input-to-state stability of stochastic inertial neural networks. Advances in Difference Equations, 2021(1).","DOI":"10.1186\/s13662-021-03586-4"},{"issue":"13","key":"10.3233\/JCM-226480_ref13","first-page":"209","article-title":"Exponential stability of a class of BAM neural networks with stochastic inertial delays","volume":"50","author":"Zhang","year":"2020","journal-title":"Mathematics in Practice and Theory"},{"issue":"1","key":"10.3233\/JCM-226480_ref14","first-page":"83","article-title":"Stability of a class of stochastic inertial delay neural networks","volume":"35","author":"Zhang","year":"2020","journal-title":"Journal of Applied Mathematics of colleges and universities series a"},{"issue":"14","key":"10.3233\/JCM-226480_ref15","first-page":"218","article-title":"Exponential synchronization of a class of neural networks with inertial stochastic delays","volume":"51","author":"Li","year":"2021","journal-title":"Mathematics in Practice and Theory"},{"issue":"7","key":"10.3233\/JCM-226480_ref16","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1109\/81.401145","article-title":"New conditions for global stability of neural networks with application to linear and quadratic programming problems","volume":"42","author":"Forti","year":"1995","journal-title":"Circuits & Systems I Fundamental Theory & Applications IEEE Transactions on"},{"key":"10.3233\/JCM-226480_ref17","unstructured":"Liao XX. Stability theory and application of dynamical systems. Beijing: National Defense Industry Press, 2000."}],"container-title":["Journal of Computational Methods in Sciences and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JCM-226480","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,21]],"date-time":"2026-04-21T22:07:22Z","timestamp":1776809242000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JCM-226480"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,4]]},"references-count":17,"journal-issue":{"issue":"2"},"URL":"https:\/\/doi.org\/10.3233\/jcm-226480","relation":{},"ISSN":["1472-7978","1875-8983"],"issn-type":[{"value":"1472-7978","type":"print"},{"value":"1875-8983","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,4]]}}}