分数级随机延迟神经网络与冲动:平均平方有限时间收缩同步
Gokul Palanisamy1, Udhayakumar Kandasamy1, Fathalla A Rihan1
1Department of Mathematical Sciences, College of Science, United Arab Emirates University, AL Ain, UAE.
Scientific reports
|December 10, 2025
概括
本研究介绍了一种混合控制框架,用于在有限时间内同步分数顺序随机延迟神经网络. 该方法通过结合连续反和冲动控制来提高稳定性和趋同性.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 随机系统 随机系统 随机系统
背景情况:
- 分数顺序系统表现出记忆和遗传性质.
- 随机延迟神经网络呈现复杂的动态.
- 有限时间同步对于实时应用程序至关重要.
研究的目的:
- 为平均平方有限时间同步 (MSFTSn) 和平均平方有限时间合同同步 (MSFTCSn) 开发一种新的混合控制框架.
- 为了解决分数顺序随机延迟神经网络 (FOSDNNs) 中的同步挑战.
主要方法:
- 整合了随机分析,基于Lyapunov的方法,分数Gronwall不等式和改进的Razumikhin框架.
- 对不连续的神经元激活函数的设定值地图理论的应用.
- 混合控制结合了连续反和冲动调节.
主要成果:
- 为FOSDNNs建立了新的同步标准.
- 混合控制策略保证了错误系统的有限时间同步.
- 与标准反方案相比,证明了稳定参数范围的扩展.
- 通过数值模拟验证了有效性和稳定性.
结论:
- 拟议的混合控制在FOSDNN中对MSFTSn和MSFTCSn有效.
- 分数导数通过结合记忆效应来增强神经网络表示.
- 该框架提供了更好的收率和增强的合同稳定性.
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