不连续反应-扩散科恩-格罗斯伯格神经网络的固定时间周期稳定
Fanchao Kong1, Quanxin Zhu2, Hamid Reza Karimi3
1School of Mathematics and Statistics, Anhui Normal University, Wuhu, Anhui 241000, China; MOE-LCSM, School of Mathematical Sciences and Statistics, Hunan Normal University, Changsha 410081, China.
概括
这项研究通过使用新的稳定性定理和延迟依赖标准,实现了延迟不连续反应扩散科恩-格罗斯伯格神经网络的固定时间稳定. 该研究引入了改进的方法,以确保网络稳定性,即使有时间延迟.
科学领域:
- 神经网络的神经网络的神经网络
- 非线性动力学是一种非线性动力学.
- 控制理论 控制理论
背景情况:
- 延迟不连续的反应-扩散科恩-格罗斯伯格神经网络呈现复杂的动态.
- 确保这些系统的稳定性,尤其是在固定的时间内,是一个重大挑战.
研究的目的:
- 研究并实现延迟不连续反应-扩散科恩-格罗斯伯格神经网络的固定时间稳定.
- 开发新的稳定性标准,这些稳定性标准不如现有方法那么保守.
主要方法:
- 开发一个新的固定时间稳定定理,使用宽松的条件和不平等技术.
- 将周期波解决方案转换为普通微分系统,使用依赖状态的切换定律.
- 微分包容理论和巧合定理的应用,以保证周期性解决方案.
- 使用新的稳定性定理来固定时间稳定周期性溶液.
主要成果:
- 建立了一个新的,改进的固定时间稳定定理.
- 网络的周期性解决方案的存在已被证明.
- 在这种背景下,第一次实现了对零周期解决方案的固定时间稳定.
- 导出了依赖延迟的标准,比以前的依赖延迟的方法提供了不太保守的结果.
结论:
- 建议的依赖延迟的标准有效地确保研究的神经网络的固定时间稳定.
- 开发的方法为稳定复杂的神经网络系统提供了不那么保守而更强大的方法.
- 数字示例验证了理论发现,并证明了延迟的影响.
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