分数阶记忆延迟BAM神经网络新的预定义时间稳定性定理及同步

IF 3.4 2区数学 Q1 MATHEMATICS, APPLIED

Communications in Nonlinear Science and Numerical Simulation Pub Date : 2025-04-19 DOI:10.1016/j.cnsns.2025.108850

Jiale Chen , Weigang Sun , Song Zheng

{"title":"分数阶记忆延迟BAM神经网络新的预定义时间稳定性定理及同步","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":"{\"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}","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

摘要

本研究介绍了一个新定理，重点是分数阶系统中的预定义时间稳定性，并将其应用于分数阶忆苦思甜延迟双向联想记忆神经网络中的预定义时间同步领域。利用分数阶微积分的固有特征和分数阶比较原理，展示了这一定理。与依赖整数阶对应定理的现有预定义时间稳定性定理不同，我们的定理采用了分数阶框架。以该定理为基础，可以开发出高效的控制器来实现预定义时间同步。我们通过两个数值实例验证了理论成果，肯定了我们方法的稳健性和适用性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

New predefined-time stability theorem and synchronization of fractional-order memristive delayed BAM neural networks

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Communications in Nonlinear Science and Numerical Simulation MATHEMATICS, APPLIED-MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

CiteScore

6.80

自引率

7.70%

发文量

378

审稿时长

78 days

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.