间隔BAM神经网络的全球强大的指数稳定性,具有多次时间变化的延迟:基于系统解决方案的直接方法
Jinbao Lan1, Xian Zhang2, Xin Wang2
1School of Mathematical Science, Heilongjiang University, Harbin, 150080, PR China.
ISA transactions
|November 11, 2023
概括
本研究引入了一种直接方法,以确保间隔双向关联记忆 (BAM) 神经网络的全球强大的指数稳定性,具有时间变化的延迟. 这些发现为稳定性分析提供了更简单,更有效的标准.
科学领域:
- 神经网络的神经网络的神经网络
- 控制理论 控制理论
- 动态系统 动态系统
背景情况:
- 双向关联记忆 (BAM) 神经网络对于关联记忆任务至关重要.
- 确保这些网络的强大的指数稳定性,特别是随着时间变化的延迟,是一个重大挑战.
- 现有的方法通常依赖于复杂的Lyapunov-Krasovskii函数,限制了实际应用.
研究的目的:
- 分析间隔BAM神经网络的全球强指数稳定性,具有多次时间变化延迟.
- 提出一种新的,直接的稳定性分析方法.
- 为了获得简化和改进的稳定性标准.
主要方法:
- 使用基于系统解决方案的直接方法.
- 建立了全球强指数稳定的足够条件.
- 该方法避免了构建Lyapunov-Krasovskii函数的需要.
主要成果:
- 足够的条件保证一个独特的和全球强大的指数稳定平衡点间隔BAM神经网络的衍生.
- 拟议的方法简化了稳定性标准的推导.
- 新的标准显示了理论和数值优势,与现有文献相比.
结论:
- 开发的直接方法为分析间隔BAM神经网络的稳定性提供了更容易访问和更有效的方法.
- 对于全球强指数稳定的衍生标准在理论上是合理的,并且在数值上得到了验证.
- 这项研究促进了对稳定的BAM神经网络设计的理解和应用.
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