{"title":"New predefined-time stability theorem and synchronization of fractional-order memristive delayed BAM neural networks","authors":"Jiale Chen , Weigang Sun , Song Zheng","doi":"10.1016/j.cnsns.2025.108850","DOIUrl":null,"url":null,"abstract":"<div><div>This study introduces a novel theorem focusing on predefined-time stability within fractional-order systems and applies it to the domain of predefined-time synchronization in fractional-order memristive delayed bidirectional associative memory neural networks. Leveraging the inherent characteristics of fractional-order calculus and the fractional-order comparison principle, this theorem is showcased. Unlike existing predefined-time stability theorems that rely on integer-order counterparts, our theorem adopts the fractional-order framework. By utilizing this theorem as a foundation, efficient controllers are developed to achieve predefined-time synchronization. The theoretical outcomes are verified through the examination of two numerical examples, affirming the robustness and applicability of our approach.</div></div>","PeriodicalId":50658,"journal":{"name":"Communications in Nonlinear Science and Numerical Simulation","volume":"148 ","pages":"Article 108850"},"PeriodicalIF":3.4000,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Communications in Nonlinear Science and Numerical Simulation","FirstCategoryId":"100","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1007570425002618","RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATHEMATICS, APPLIED","Score":null,"Total":0}
引用次数: 0
Abstract
This study introduces a novel theorem focusing on predefined-time stability within fractional-order systems and applies it to the domain of predefined-time synchronization in fractional-order memristive delayed bidirectional associative memory neural networks. Leveraging the inherent characteristics of fractional-order calculus and the fractional-order comparison principle, this theorem is showcased. Unlike existing predefined-time stability theorems that rely on integer-order counterparts, our theorem adopts the fractional-order framework. By utilizing this theorem as a foundation, efficient controllers are developed to achieve predefined-time synchronization. The theoretical outcomes are verified through the examination of two numerical examples, affirming the robustness and applicability of our approach.
期刊介绍:
The journal publishes original research findings on experimental observation, mathematical modeling, theoretical analysis and numerical simulation, for more accurate description, better prediction or novel application, of nonlinear phenomena in science and engineering. It offers a venue for researchers to make rapid exchange of ideas and techniques in nonlinear science and complexity.
The submission of manuscripts with cross-disciplinary approaches in nonlinear science and complexity is particularly encouraged.
Topics of interest:
Nonlinear differential or delay equations, Lie group analysis and asymptotic methods, Discontinuous systems, Fractals, Fractional calculus and dynamics, Nonlinear effects in quantum mechanics, Nonlinear stochastic processes, Experimental nonlinear science, Time-series and signal analysis, Computational methods and simulations in nonlinear science and engineering, Control of dynamical systems, Synchronization, Lyapunov analysis, High-dimensional chaos and turbulence, Chaos in Hamiltonian systems, Integrable systems and solitons, Collective behavior in many-body systems, Biological physics and networks, Nonlinear mechanical systems, Complex systems and complexity.
No length limitation for contributions is set, but only concisely written manuscripts are published. Brief papers are published on the basis of Rapid Communications. Discussions of previously published papers are welcome.