Fixed-Time Stabilization of Reaction-Diffusion Memristive Neural Networks Under Multiple Stochastic Noise and Deception Attacks

Fixed-Time Stabilization of Reaction-Diffusion Memristive Neural Networks Under Multiple Stochastic Noise and Deception Attacks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Fixed-Time Stabilization of Reaction-Diffusion Memristive Neural Networks Under Multiple Stochastic Noise and Deception Attacks Yangyang Li, Guici Chen, Leimin Wang, Guodong Zhang, Shiping Wen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7554698/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This paper investigates the fixed-time mean-square exponential stabilization problem of reaction-diffusion memristive neural networks (RDMNNs) influenced by multiple stochastic noise. An operator is constructed by averaging the spatial distributed parameters, and a comprehensive theoretical framework is developed, which can accurately describe the complex spatiotemporal dynamics of such systems.Under this framework, a rigorous operator-based Itô formula is derived, which applies to mem-ristive neural networks exhibiting reaction-diffusion behavior. By incorporating the interval matrix method, sufficient conditions for guaranteeing fixed-time mean-square exponential stabilization are obtained. Based on control cost and timeliness, and considering complex communication environments , three theorems are proposed. Finally, a simulation case is presented to analyze the control effects and costs of the controllers designed by the three theorems. Simulation results demonstrate that the proposed controllers remain effective even under network attacks. Reaction-Diffusion Multiple stochastic noise Interval matrix method Fixed-time stabilization Deception attacks Full Text Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.png Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 11 Dec, 2025 Reviews received at journal 22 Oct, 2025 Reviews received at journal 20 Oct, 2025 Reviewers agreed at journal 17 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 14 Sep, 2025 Submission checks completed at journal 14 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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