School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
School of Automation, China University of Geosciences, Wuhan 430074, China; Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan 430074, China.
Neural Netw. 2018 Dec;108:527-532. doi: 10.1016/j.neunet.2018.09.011. Epub 2018 Sep 29.
This paper is concerned with the reachable set estimation for Markovian jump neural networks with time-varying delay and bounded peak inputs. The objective is to find a description of a reachable set that is containing all reachable states starting from the origin. In the framework of Lyapunov-Krasovskii functional method, an appropriate Lyapunov-Krasovskii functional is constructed firstly. Then by using the Wirtinger-based integral inequality and the extended reciprocally convex matrix inequality, an ellipsoidal description of the reachable set for the considered neural networks is derived. Finally, a numerical example with simulation results is provided to verify the effectiveness of our results.
本文研究了具有时变时滞和有界峰值输入的马尔可夫跳跃神经网络的可达集估计。目的是找到一个可达集的描述,该描述包含从原点开始的所有可达状态。在 Lyapunov-Krasovskii 泛函方法的框架下,首先构造了一个适当的 Lyapunov-Krasovskii 泛函。然后,利用基于 Wirtinger 的积分不等式和扩展的互凸矩阵不等式,得到了所考虑神经网络可达集的椭球描述。最后,通过一个数值例子和仿真结果验证了我们结果的有效性。