具有时间变化的延迟的中性类型复杂值神经网络的有限时间被动性
Haydar Akca1, Chaouki Aouiti2, Farid Touati2
1Abu Dhabi University, College of Arts and Sciences, Department of Applied Sciences and Mathematics, Abu Dhabi, UAE.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
这项研究探讨了具有延迟的复杂值神经网络中的有限时间被动性. 新的标准确保了有限时间的局限性和被动性,通过数值示例来验证.
科学领域:
- 具有复杂价值的神经网络.
- 控制理论 控制理论 控制理论
- 非线性系统是非线性系统.
背景情况:
- 神经网络在AI中至关重要,但它们在延迟时的稳定性是复杂的.
- 被动性是系统稳定性和能量消耗分析的关键.
- 具有时间变化的延迟的中性类型系统存在独特的分析挑战.
研究的目的:
- 调查中性类型复杂值神经网络的有限时间被动性.
- 开发有限时间受限性 (FTB) 和有限时间被动性 (FTP) 的充分条件.
- 分析时间变化的延迟对网络稳定性和被动性的影响.
主要方法:
- 稳定性分析的Lyapunov功能方法.
- 维廷格式的不平等技术来处理延迟.
- 线性矩阵不等式 (LMIs) 用于导出足够条件.
主要成果:
- 建立了有限时间局限性 (FTB) 的新的充分条件.
- 获得了有限时间被动性 (FTP) 的新标准.
- 提出的方法有效地分析特定神经网络模型的稳定性.
结论:
- 导出条件保证神经网络模型的有限时间被动性和局限性.
- 利亚普诺夫功能和LMI方法为分析这些系统提供了一个强大的框架.
- 数字模拟证实了理论发现和提出的标准的有效性.
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