通过改进的矩阵值多项式不等式,通过延迟神经网络的稳定性和被动性分析
Guo-Qiang Kong1, Liang-Dong Guo1
1School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, PR China.
概括
本文介绍了一个新的Lyapunov-Krasovskii函数,用于分析延迟的神经网络. 改进的方法为这些复杂的系统提供了不那么保守的稳定性和被动性标准.
科学领域:
- 控制理论 控制理论
- 人工神经网络的人工神经网络
- 系统工程 系统工程
背景情况:
- 延迟神经网络 (DNN) 在建模复杂系统中至关重要.
- 评估DNN的稳定性和被动性对于可靠的应用是必不可少的.
- 现有的DNN分析方法往往需要保守的限制.
研究的目的:
- 开发新的标准来评估DNN的稳定性和被动性.
- 克服DNN现有的分析方法的局限性.
- 为DNN分析提出一个不那么保守的框架.
主要方法:
- 构建一个新的Lyapunov-Krasovskii函数 (LKF),避免多重积分.
- 应用一个改进的矩阵值多项式不等式 (MVPI).
- 删除与MVPI中的斜对称矩阵相关的约束.
主要成果:
- 为DNN稳定性和被动性分析开发了一种新的LKF.
- 改进的MVPI消除了以前的约束,导致了不那么保守的结果.
- 拟议的标准增强了对DNN稳定性和被动性的分析.
结论:
- 这种新的方法为DNN提供了不那么保守的稳定性和被动性标准.
- 该方法的可行性和优越性通过三个说明性示例得到证实.
- 这项工作推进了理解延迟神经网络动态的分析工具.
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