基于延迟的memristor的惯性神经网络通过非减少顺序方法的输入到状态稳定性
Yuxin Jiang1, Song Zhu1, Xiaoyang Liu2
1School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China.
概括
这项研究建立了基于延迟memristor的惯性神经网络 (DMINNs) 的输入到状态稳定性 (ISS) 的新标准. 这些发现为分析这些复杂的动态系统提供了更严格和更实用的方法.
科学领域:
- 计算神经科学是一种神经科学.
- 非线性动态系统非线性动态系统
- 控制理论 控制理论
背景情况:
- 基于memristor的惯性神经网络 (MINNs) 对于模拟复杂的大脑功能至关重要.
- 纳入时间延迟 (DMINNs) 在稳定性分析中带来了重大挑战.
- 现有的方法经常简化模型,可能会失去物理相关性.
研究的目的:
- 研究基于延迟memristor的惯性神经网络 (DMINNs) 的输入到状态稳定性 (ISS).
- 开发新的稳定性标准,既依赖延迟,又不依赖延迟.
- 为了更严格的分析,采用非减少订单方法.
主要方法:
- 使用非光滑分析和稳定性理论.
- 为稳定性证明构建多个Lyapunov函数.
- 直接将非减少订单分析应用于DMINN模型.
主要成果:
- 在DMINNs中为ISS获得了新的延迟依赖性和延迟独立性标准.
- 非减少订单方法被证明是更严格和计算效率更高的.
- 这些结果扩展和补充了关于记忆神经网络动态的现有研究.
结论:
- 开发的标准提供了可靠的条件,以确保DMINNs的稳定性.
- 非减少顺序方法提高了分析的实际适用性和物理解释性.
- 这项工作有助于更深入地了解复杂的记忆神经系统中的稳定性.
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