通过DAEIC对延迟的半马科维神经网络进行指数异步稳定
IEEE transactions on neural networks and learning systems
|September 18, 2024
概括
本研究提出了一种新的离散自适应事件触发的神经网络的冲动控制,具有半马尔科夫跳跃参数和时间变化的延迟,确保指数级异步稳定.
科学领域:
- 控制系统工程 控制系统工程
- 计算神经科学是一种神经科学.
- 网络化系统 网络化系统
背景情况:
- 具有半马尔科夫跳跃 (SMJ) 参数和附加时间变化延迟 (ATD) 的神经网络 (NN) 存在复杂的稳定性挑战.
- 控制器增益和系统结构中的明确的SMJ参数反映了现实世界的场景.
- 现有的控制方法可能会造成很大的通信负载.
研究的目的:
- 解决SMJ参数和ATDs的NNs的指数异步稳定 (EAS) 的问题.
- 提出一个离散的自适应事件触发式冲动控制 (DAEIC) 方案,以减少通信负载.
- 开发一个灵活循环的莱阿普诺夫-克拉索夫斯基函数 (LLKF) 以改进分析.
主要方法:
- 一个具有自适应更新规则 (AUR) 的新型DAEIC计划,用于动态值调整.
- 构建一个灵活的LLKF,以结合系统动态,延迟和控制参数.
- 应用不平等分析技术与LLKF和DAEIC方案相结合.
主要成果:
- 获得了新的理论结果,保证了所考虑的NN的EAS.
- 拟议的DAEIC方案有效地管理通信负载,同时确保系统稳定性.
- 该LLKF提供了一个全面的框架,用于分析具有异质参数和延迟的系统.
结论:
- 开发的控制策略确保了对具有复杂参数和延迟动态的神经网络的EAS.
- 事件触发式方法显著降低了通信负担.
- 通过三个例子进行验证证实了拟议方法的有效性.
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