延迟神经网络的改善稳定性标准:进一步利用有关时间变化延迟和激活功能的信息
IEEE transactions on cybernetics
|February 18, 2026
概括
新方法通过使用时间变化的延迟和激活函数信息来改善延迟神经网络 (DNN) 的稳定性标准. 这些技术增强了系统分析,并为复杂网络提供了更准确的稳定性评估.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 计算神经科学是一种神经科学.
背景情况:
- 延迟神经网络 (DNN) 在建模复杂系统中至关重要,但由于时间延迟,在稳定性分析中面临挑战.
- 现有的稳定性标准往往无法充分利用系统信息,导致保守的结果.
研究的目的:
- 为DNN开发新的,低保守的稳定性标准.
- 加强在稳定性分析中利用可变时间延迟和激活功能的信息.
主要方法:
- 在Lyapunov-Krasovskii函数 (LKF) 中引入延迟产品术语,以纳入时间变化的延迟信息.
- 开发基于延长矩阵注射的转换,使用延迟导数依赖的松矩阵.
- 在LKF中增加激活函数术语,包括使用部门约束信息的非线性函数依赖术语.
主要成果:
- 为DNN推导了几个改进的稳定性标准.
- 通过有效使用系统特定信息来证明增强的稳定性分析.
- 用两个说明性示例验证拟议的方法.
结论:
- 提出的技术有效地改善了DNN的稳定性标准.
- 延迟和激活功能信息的增强利用导致不那么保守和更准确的稳定性评估.
- 开发的方法为分析复杂的延迟动态系统的稳定性提供了显著的优势.
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