Liu Hongjian, Wang Zidong, Shen Bo, Liu Xiaohui
IEEE Trans Neural Netw Learn Syst. 2018 Aug;29(8):3726-3737. doi: 10.1109/TNNLS.2017.2728639. Epub 2017 Sep 1.
In this paper, the event-triggered state estimation problem is investigated for a class of discrete-time stochastic memristive neural networks (DSMNNs) with time-varying delays and missing measurements. The DSMNN is subject to both the additive deterministic disturbances and the multiplicative stochastic noises. The missing measurements are governed by a sequence of random variables obeying the Bernoulli distribution. For the purpose of energy saving, an event-triggered communication scheme is used for DSMNNs to determine whether the measurement output is transmitted to the estimator or not. The problem addressed is to design an event-triggered estimator such that the dynamics of the estimation error is exponentially mean-square stable and the prespecified disturbance rejection attenuation level is also guaranteed. By utilizing a Lyapunov-Krasovskii functional and stochastic analysis techniques, sufficient conditions are derived to guarantee the existence of the desired estimator, and then, the estimator gains are characterized in terms of the solution to certain matrix inequalities. Finally, a numerical example is used to demonstrate the usefulness of the proposed event-triggered state estimation scheme.
本文研究了一类具有时变延迟和测量缺失的离散时间随机忆阻神经网络（DSMNNs）的事件触发状态估计问题。DSMNN 同时受到加性确定性干扰和乘性随机噪声的影响。测量缺失由服从伯努利分布的随机变量序列控制。为了节能，DSMNNs 采用事件触发通信方案来确定测量输出是否传输到估计器。所解决的问题是设计一个事件触发估计器，使得估计误差的动态在指数均方意义下稳定，并且还能保证预设的干扰抑制衰减水平。通过利用 Lyapunov-Krasovskii 泛函和随机分析技术，推导出了保证所需估计器存在的充分条件，然后根据某些矩阵不等式的解来表征估计器增益。最后，通过一个数值例子来说明所提出的事件触发状态估计方案的有效性。
