延迟依赖的Lurie-Postnikov类型Lyapunov-Krasovskii函数用于对离散时间延迟神经网络的稳定性分析
Ke-You Xie1, Chuan-Ke Zhang1, Sangmoon Lee2
1School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan 430074, China.
概括
这项研究通过结合非线性激活函数信息来增强离散时间神经网络稳定性分析. 新方法减少了保守主义,改善了变时延迟网络的稳定性标准.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 动态系统 动态系统
背景情况:
- 离散时间神经网络在各种应用中至关重要.
- 稳定性分析通常因时间变化的延迟和非线性而复杂化.
- 现有的方法主要集中在延迟信息,可能忽视其他因素.
研究的目的:
- 调查非线性激活函数与部门限制对网络稳定的影响.
- 开发一种使用Lyapunov-Krasovskii函数 (LKF) 进行稳定性分析的补偿技术.
- 为离散时间延迟神经网络推导出不那么保守的稳定性标准.
主要方法:
- 使用Lyapunov-Krasovskii函数 (LKF) 进行稳定性分析.
- 建议依赖于延迟的Lurie-Postnikov类型的整体条款来增强LKF的构建.
- 将非线性激活函数的部门约束纳入分析中.
主要成果:
- 介绍了一种新的 LKF 结构,集成非线性激活函数信息.
- 对于离散时间延迟网络来说,获得了更好的,不那么保守的稳定性标准.
- 数字示例验证了通过拟议的方法减少保守主义.
结论:
- 拟议的方法有效地利用非线性激活函数信息进行稳定性分析.
- 整合依赖延迟的积分项和部门约束导致稳定性标准的改进.
- 这种方法为离散时间神经网络稳定性提供了更准确,更不保守的评估.
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