延迟神经网络的放松稳定性标准使用了依赖延迟参数的松矩阵
Hong-Bing Zeng1, Zong-Jun Zhu2, Wei Wang1
1School of Electrical and Information Engineering, Hunan University of Technology, Zhuzhou 412007, China.
概括
这项研究提出了神经网络的新稳定性标准,这些神经网络具有变化时间的延迟. 增强的Lyapunov-Krasovskii功能 (LKF) 方法减少了保守主义,改善了复杂系统的稳定性分析.
科学领域:
- 控制理论 控制理论
- 计算神经科学是一种神经科学.
- 系统工程 系统工程
背景情况:
- 具有时间变化的延迟的神经网络在稳定性分析中提出了挑战.
- 现有的稳定性标准可能过于保守,限制了它们的实际应用.
研究的目的:
- 为具有时间变化延迟的神经网络制定不那么保守的稳定性标准.
- 提高神经网络系统中稳定性分析的准确性和适用性.
主要方法:
- 构建一个增强的Lyapunov-Krasovskii函数 (LKF) 的延迟-产品条款.
- 参数依赖的松散矩阵的引入到积分不等式和S程序中.
- 利亚普诺夫-克拉索夫斯基定理用于稳定性分析的应用.
主要成果:
- 与现有方法相比,实现了更宽松的稳定性标准.
- 通过数值示例展示了减少的保守主义.
- 验证了拟议的增强LKF方法的有效性.
结论:
- 拟议的方法在减少稳定性标准的保守主义方面提供了显著的改进.
- 增强的Lyapunov-Krasovskii功能方法为分析时间延迟神经网络提供了更有效的工具.
- 这项工作有助于更强大,更可靠的神经网络系统设计.
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