Event-Based State Estimation for Neural Network With Time-Varying Delay and Infinite-Distributed Delay
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摘要: 研究了事件触发机制下混合时滞神经网络的状态估计问题.通过引入依赖于测量输出且具有指数衰减特性的阈值函数,设计了新的事件触发机制来降低采样和通信频率.综合混合时延和事件触发特性, 建立了新的状态估计误差系统.采用Lyapunov函数和不等式技术, 建立了误差系统指数稳定性条件, 分析并排除了事件触发机制中的Zeno现象.最后通过例子验证了理论方法的有效性.
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关键词:
- 混合时滞 /
- 状态估计 /
- 事件触发控制 /
- Zeno行为 /
- Lyapunov函数
Abstract: The event-based state estimation problem was investigated for a class of neural networks with mixed delays. A novel event-triggering scheme depending on both the output and exponential decay function was designed to reduce the frequency of updating. In view of both the mixed delays and the event-triggering properties, a new state estimation error system was built. The exponential stability of the error system was derived with the Lyapunov function and the inequality technique. The Zeno phenomenon was analyzed and excluded. Finally, a numerical example and its simulations were presented to illustrate the effectiveness of the proposed approach.-
Key words:
- mixed delay /
- state estimation /
- event-triggering control /
- Zeno behavior /
- Lyapunov function
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