通过基于分离矩阵的积分不等式,提高了时间延迟神经网络的指数稳定性
Yuanyuan Zhang1, Xinzuo Ma1, Seakweng Vong1
1organization=Department of Mathematics, University of Macau, addressline=Avenida da Universidade, city=Macau, country=China.
概括
这项研究引入了一种用于分析具有时间延迟的神经网络指数稳定的新方法. 该方法通过结合更多的延迟信息来增强稳定性标准,从而提高性能和效率.
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.
- 人工智能的人工智能
背景情况:
- 有时间延迟的神经网络对于建模复杂的动态系统至关重要.
- 评估这些网络的稳定性对于可靠运行至关重要.
- 稳定性分析的传统方法经常受到保守主义的影响.
研究的目的:
- 为具有时间延迟的神经网络的指数稳定性制定不那么保守的标准.
- 通过结合更多的延迟信息来提高稳定性分析的准确性.
- 为了提高神经网络稳定性的最大允许延迟极限.
主要方法:
- 提出了一个基于分离矩阵的全方位不等式.
- 构建一个包含基于分离矩阵的积分的Lyapunov-Krasovskii函数.
- 采用使用二次数负确定的线性矩阵不等式框架.
主要成果:
- 与传统方法相比,拟议的方法显著减少了保守主义.
- 建立了新的稳定性标准,准确度提高.
- 在确定最大允许延迟极限方面表现出优越性.
结论:
- 这种新的方法提供了一种不那么保守而更有效的方法来分析具有时间延迟的神经网络稳定性.
- 这些发现有助于时间延迟神经网络的可靠设计和应用.
- 数字示例验证了该方法的有效性和计算效率.
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