循环神经网络的稳定性分析,时间延迟变化,基于灵活的负判定二次函数方法
IEEE transactions on neural networks and learning systems
|November 3, 2023
概括
本研究提出了一种新方法来分析具有时间变化的延迟的循环神经网络 (RNN) 的稳定性. 这些发现为这些复杂系统提供了一个不那么保守的稳定性标准.
科学领域:
- 控制理论 控制理论
- 人工智能的人工智能
- 神经网络的神经网络的神经网络
背景情况:
- 循环神经网络 (RNN) 对于连续的数据处理至关重要.
- 具有不同时间延迟的RNN的稳定性分析是一个重大挑战.
- 现有的稳定性分析方法可能过于保守.
研究的目的:
- 调查RNN的稳定性问题与时间变化的延迟.
- 制定一个不那么保守的稳定性标准.
- 证明拟议方法的有效性.
主要方法:
- 引入灵活性因子,用于一种新的负判定二次函数方法.
- 将积分不等式应用到Lyapunov-Krasovskii函数 (LKF) 导数的结合上.
- 对延迟RNN的稳定性标准的推导.
主要成果:
- 提出了一种灵活的负确定二次函数方法,减少保守主义.
- 确定了LKF衍生品的准确上限.
- 对于延迟的RNN来说,一个不那么保守的稳定性标准是得到的.
结论:
- 拟议的方法为具有时间变化的延迟的RNN提供了改进的稳定性分析.
- 衍生稳定性标准比现有方法要保守得多.
- 数字示例验证了拟议方法的有效性和优越性.
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