分数顺序记忆模糊神经网络的有限时间同步:基于事件的控制与线性测量误差
概括
这项研究引入了一种新的事件触发的有限时间控制,用于分数顺序的记忆神经网络. 这种新的方法简化了计算,避免了Zeno行为,提供了不那么保守的同步标准.
科学领域:
- 控制理论 控制理论
- 人工神经网络的人工神经网络
- 分数微积分的计算.
背景情况:
- 分数顺序记忆神经网络 (FOMNNs) 是复杂的系统,需要先进的控制策略.
- 有限时间同步 (F-tS) 对这些网络的性能至关重要.
- 现有的基于事件的控制方案通常涉及复杂的非线性错误函数.
研究的目的:
- 为FOMNN开发一个新的事件触发的有限时间控制策略,使用切换模糊术语.
- 为分数顺序系统 (FSs) 提出一个新的有限时间分析框架,以导出F-tS标准.
- 确保在拟议的控制方案中排除Zeno行为.
主要方法:
- 在事件触发机制 (ETM) 中开发线性测量误差函数.
- 使用线性矩阵不等式 (LMIs) 制定F-tS标准.
- 引入FS的新型有限时间分析框架,包括新的不等式和基于加权规范的利亚普诺夫函数.
主要成果:
- 拟议的事件触发控制策略实现了FOMNNs的有限时间同步.
- ETM的线性简化了计算,并保证了没有Zeno行为.
- 与现有方法相比,新的分析框架产生了不那么保守的F-tS标准.
结论:
- 开发的由事件触发的有限时间控制策略对于FOMNNs来说是有效和优越的.
- 新的分析框架为FS中的F-tS分析提供了一种更有效,更少保守的方法.
- 这项研究推进了复杂的分数顺序动态系统的控制.
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