School of Mathematics and Statistics, Southwest University, Chongqing 400715, China.
School of Mathematics, Southeast University, Nanjing 210096, China; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Neural Netw. 2018 Mar;99:79-91. doi: 10.1016/j.neunet.2017.12.014. Epub 2018 Jan 9.
This paper addresses the problem of H state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results.
本文针对一类具有时变时滞的随机忆阻神经网络的 H 状态估计问题进行了研究。在 Filippov 解的框架下，将随机忆阻神经网络转化为具有区间参数的系统。本文首次研究了连续时间 Ito 型随机忆阻神经网络的 H 状态估计问题。通过 Lyapunov 泛函和一些随机技术，得出了确保估计误差系统在给定 H 性能下均方渐近稳定的充分条件。以线性矩阵不等式（LMI）的形式给出了状态估计增益的显式表达式。与其他结果相比，我们的结果有效地降低了控制增益和控制成本。最后，通过数值模拟验证了理论结果的有效性。
