Fixed-time stabilization of Caputo fractional-order fuzzy delayed recurrent neural networks with reaction-diffusion terms

Fixed-time stabilization of Caputo fractional-order fuzzy delayed recurrent neural networks with reaction-diffusion terms | 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 Caputo fractional-order fuzzy delayed recurrent neural networks with reaction-diffusion terms Hediene Jallouli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6653431/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this survey, a class of Caputo fractional-order fuzzy recurrent neural networks with reaction-diffusion terms and time-varying delays is considered. Firstly, the fixed-time stabilization of the origin of the considered neural networks is investigated by designing a novel control strategy, which can improve not only the previous ones with sign function greatly, but also can reduce the chattering phenomenon. Secondly, the finite-time stabilization is analyzed by using the finite-time stability theorem, Lyapunov functional, and inequalities techniques. Finally, two numerical examples are presented to show that the proposed method is effective. MSC Classification: 34A08 , 93B52 , 34H15 reaction-diffusion neural networks fixed-time stabilization Caputo derivative Lyapunov function Fuzzy neural networks finite-time stabilization Full Text Cite Share Download PDF Status: Posted Version 1 posted 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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