通过新型矩阵分离传说不平等来延迟基于memristor的神经网络系统的放松稳定性标准
IEEE transactions on neural networks and learning systems
|October 23, 2024
概括
这项研究增强了基于memristor的神经网络 (MNN) 稳定性分析,具有时间变化的延迟. 新型不等式和利亚普诺夫-克拉索夫斯基函数减少了对更可靠的MNN系统的保守主义.
科学领域:
- 人工智能的人工智能
- 控制理论 控制理论
- 材料科学 材料科学 材料科学
背景情况:
- 基于memristor的神经网络 (MNN) 提供了先进的计算能力.
- 随时间变化的MNN延迟对系统稳定性构成重大挑战.
- 现有的稳定性分析方法往往表现为保守主义.
研究的目的:
- 开发新的分析技术,用于在MNN中进行稳定性评估,延迟时间不同.
- 为了减少MNN的稳定条件的保守主义.
- 为MNN稳定性提供实用和可执行的标准.
主要方法:
- 提出了一种新的矩阵分离,用于界定积分项的莱根德不等式.
- 引入了延迟依赖矩阵来处理互换项.
- 开发了一个新的Lyapunov-Krasovskii函数,包含增强积分和延迟积分.
- 使用自由权重矩阵,零和方程和S程序.
主要成果:
- 在增强型积分项上实现了更严格的等级约束.
- 为稳定性分析推导出可实施的不平等条件.
- 拟议的方法在稳定条件下显著减少了保守主义.
- 通过三个数值示例和模拟进行验证.
结论:
- 这种新的方法为MNN提供了不那么保守的稳定性条件,并具有时间变化的延迟.
- 这些发现有助于MNN的可靠设计和应用.
- 提出的方法为复杂动态系统中的稳定性分析提供了强大的框架.
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