Input-to-state Stabilization of Delayed Semi-Markovian Jump Neural Networks Via Sampled-Data Control

作     者：He, Ling Wu, Wenhuang Yao, Guangshun Zhou, Jianping 

作者机构：Anhui Univ Technol Sch Comp Sci & Technol Maanshan 243032 Peoples R China Chuzhou Univ Sch Comp & Informat Engn Chuzhou 239000 Peoples R China 

出 版 物：《NEURAL PROCESSING LETTERS》 (神经处理通讯)

年 卷 期：2023年第55卷第3期

页      面：3245-3266页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Natural Science Foundation of the Anhui Higher Education Institutions [KJ2020A0248  KJ2021ZD0129] 

主　　题：Input-to-state stability Stabilization Semi-Markovian process Neural network Sampled-data control 

摘      要：This paper discusses the sampled-data input-to-state stabilization for delayed semi-Markovian jump neural networks subject to external disturbance. First, a hybrid closed-loop system is formulated, which contains continuous-time state signal, disturbance input signal, discrete-time control signal, and jumping parameters of the semi-Markovian process. Then, two time-dependent and mode-dependent Lyapunov functionals are constructed corresponding to different assumptions about the activation functions. Subsequently, two sufficient conditions concerning the sampled-data controller design are derived to ensure the mean-square input-to-state stability for the hybrid closed-loop system by utilizing the proposed Lyapunov functionals, a few inequalities, as well as some stochastic analysis techniques. It is worth remarking that the present conditions are capable of ensuring mean-square exponential stability of the closed-loop system in the absence of external disturbances. Lastly, a numerical example is employed to verify the validity of the proposed input-to-state stabilization methods.