Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing 163318, China; Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing 163318, China.
Department of Computer Science, Brunel University London, Uxbridge, Middlesex, UB8 3PH, United Kingdom.
Neural Netw. 2020 Oct;130:143-151. doi: 10.1016/j.neunet.2020.06.023. Epub 2020 Jul 6.
In this paper, the protocol-based remote state estimation problem is considered for a kind of delayed artificial neural networks. The random time-varying delays fall into certain intervals with known probability distributions. For the sake of reducing the data collisions in communication channel from the sensors to the estimator, the stochastic communication protocol (SCP) is employed to decide which sensor is allowed to transmit its data to the remote estimator through the channel at each fixed instant. The scheduling principle of the SCP is governed by a Markov chain whose transition probability is allowed to be uncertain so as to reflect the possible imprecision when implementing the SCP. Through a combination of Lyapunov-Krasovskii functional method and the stochastic analysis technique, a sufficient criterion is obtained for the existence of the desired remote state estimator ensuring that the corresponding augmented estimation error dynamics is asymptotically stable with a prescribed H performance index. Furthermore, the estimator parameter is acquired by solving a convex optimization problem. Finally, the validity of the established theoretical results is demonstrated via a numerical simulation example.
本文针对一类时滞人工神经网络，研究了基于协议的远程状态估计问题。随机时变时滞落入具有已知概率分布的某些区间内。为了减少从传感器到估计器的通信信道中的数据冲突，采用随机通信协议（SCP）来决定在每个固定时刻哪个传感器被允许通过信道将其数据传输到远程估计器。SCP 的调度原则由一个马尔可夫链来控制，其转移概率是允许不确定的，以反映在实施 SCP 时可能存在的不精确性。通过 Lyapunov-Krasovskii 泛函方法和随机分析技术的结合，得到了存在期望远程状态估计器的充分条件，保证了相应的增广估计误差动力学系统具有给定的 H 性能指标渐近稳定。此外，通过求解凸优化问题得到了估计器参数。最后，通过一个数值仿真示例验证了所建立的理论结果的有效性。
