改进了固定时间稳定性分析和用于同步不连续复杂值模糊细胞神经网络的应用
Jingsha Zhang1, Jing Yang2, Qintao Gan2
1Hebei Provincial Innovation Center for Wireless Sensor Network Data Application Technology, Hebei Provincial Key Laboratory of Information Fusion and Intelligent Control, Hebei Normal University, Shijiazhuang 050024, China.
这项研究为不连续系统引入了新的固定时间 (FXT) 稳定性定理,提高了复杂值模糊细胞神经网络 (CVFCNNs) 的精度和降低了保守性,以及它们的同步.
科学领域:
- 控制理论 控制理论
- 非线性系统是非线性系统.
- 人工神经网络的人工神经网络
背景情况:
- 不连续系统在稳定性分析中存在挑战.
- 现有的固定时间稳定方法往往需要严格的条件.
- 复杂值模糊细胞神经网络 (CVFCNNs) 广泛使用,但需要强大的稳定性分析.
研究的目的:
- 为不连续系统提出新的固定时间 (FXT) 稳定性定理.
- 为稳定性分析开发更宽松的条件,消除传统的Vt>1和0
- 为了研究CVFCNN与不连续激活函数的FXT同步.
主要方法:
- 开发新的FXT稳定性定理,使用新的优化方法.
- 应用一种非传统的非分离方法来分析CVFCNNs.
- 同时讨论FXT同步.
主要成果:
- 与现有方法相比,拟议的定理提供了更宽松的条件.
- 非分离方法显著减少了稳定性结果的保守性.
- 提高CVFCNN的结算时间 (ST) 的精度及其同步.
- 数字模拟验证了理论发现.
结论:
- 新的FXT稳定性定理是有效的,并且不那么保守.
- 提出的方法在分析和同步CVFCNNs方面提供了卓越的性能.
- 这项工作促进了对复杂系统中FXT稳定性的理解和应用.
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