结合Lyapunov函数的方法用于分析中性Cohen-Grossberg神经网络的稳定性,具有多次延迟
Ozlem Faydasicok1, Sabri Arik2
1Department of Mathematics, Faculty of Science Istanbul University, Vezneciler, Istanbul, Turkey.
概括
本研究为具有时间和中性延迟的科恩-格罗斯伯格神经网络引入了新的稳定性标准. 这些发现为这些复杂的神经系统的全球稳定提供了改善的,与延迟无关的条件.
科学领域:
- 计算神经科学是一种神经科学.
- 动态系统理论 动态系统理论
- 人工智能的人工智能
背景情况:
- 科恩-格罗斯伯格神经网络是计算神经科学的基本模型.
- 分析这些网络的稳定性对于理解它们的行为和应用至关重要.
- 现有的方法经常与具有恒定时间和中性延迟的网络扎.
研究的目的:
- 为科恩-格罗斯伯格神经网络的概括类开发新的稳定性标准.
- 解决包含常量时间和中性延迟参数的网络.
- 为了建立独立于延迟的稳定性条件.
主要方法:
- 应用联合利亚普诺夫函数的方法.
- 系统地结合了各种利亚普诺夫函数的组合.
- 分析具有利普希茨连续激活函数的网络.
主要成果:
- 新的标准确保了Cohen-Grossberg神经网络模型的全球稳定性.
- 稳定性条件独立于时间和中性延迟参数.
- 导出条件的特点仅仅是网络的常数参数.
结论:
- 拟议的稳定性标准比现有方法提供了显著的进步.
- 这些结果为网络稳定性分析提供了替代性和可能更适用的条件.
- 一个数值示例表明了新发现的实际实用性.
相关概念视频
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