动态事件触发的H∞状态估计对于具有混合时间延迟的离散时间复杂值的记忆神经网络
Yufei Liu1, Bo Shen2, Hongjian Liu3
1School of Electrical Engineering, Anhui Polytechnic University, Wuhu 241000, China; Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Anhui Polytechnic University, Wuhu 241000, China.
本研究介绍了一种H-无限状态估计方法,用于具有延迟的离散时间复杂值的记忆神经网络. 引入了一个新的动态事件触发方案,以减少通信负载并确保系统稳定性.
科学领域:
- 控制理论 控制理论
- 神经网络的神经网络的神经网络
- 非线性系统是非线性系统.
背景情况:
- 记忆神经网络 (MNN) 对于先进的计算至关重要.
- 复杂值的MNNs (CVMNNs) 提供了增强的功能.
- 在有延迟的CVMNN中进行国家估计是具有挑战性的.
研究的目的:
- 为离散时间的CVMNNs开发一个H-infinity状态估计器.
- 将分布式和时间变化的延迟纳入实际建模.
- 引入一个动态事件触发的方案,以实现高效的沟通.
主要方法:
- 将CVMNN转换为增强现实和虚拟系统.
- 动态事件触发状态估计器的设计.
- 莱普诺夫函数用于保证估计误差系统的稳定性.
主要成果:
- 由此可得出一个足够的条件来证明估计误差的非对称稳定性.
- 通过矩阵不等式获得状态估计器的明确表达式.
- 拟议的方法通过模拟示例来验证.
结论:
- 新型动态事件触发的H-无限状态估计对CVMNNs有效.
- 这种方法成功地处理了延迟,并减少了通信负担.
- 该方法确保了状态估计误差的稳定性.
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