一类随机模糊细胞神经网络的输入对状态稳定性分析  

Input-to-state Stability of a Class of Stochastic Fuzzy Cellular Neural Networks

作　　者：周伟松[1] 王兴武 吴东海 曾豪 ZHOU Weisong;WANG Xingwu;WU Donghai;ZENG Hao(Key Laboratory of Industrial Internet of Things & Networked Control/Key Lab of Intelligent Analysis and Decision on Complex Systems,Chongqing University of Posts and Telecommunications, Chongqing 400065)

机构地区：[1]重庆邮电大学工业物联网与网络化控制重点实验室/复杂系统智能分析与决策重点实验室,重庆400065

出　　处：《四川师范大学学报（自然科学版）》2019年第1期104-110,共7页Journal of Sichuan Normal University（Natural Science）

基　　金：重庆市教委一般科研项目(KJ1704099)

摘　　要：研究一类带时变系数的随机泛函模糊细胞神经网络在均方意义下的指数输入对状态稳定性.利用It8公式、Razumikhin技巧和建立Halanay微分不等式,得到该系统在均方意义下的指数输入对状态稳定性的2个充分条件.最后,给出一个数值仿真例子用以来说明得到的判据的正确性和有效性.In this paper,we investigate the mean-square exponential input-to-state stability of a class of stochastic fuzzy cellular neural networks with time-varying coefficients.By using Ito formula,Razumikhin technique and constructing Halanay-type differential equalities,two sufficient conditions that guarantee the mean-square exponential input-to-state stability of our considered neural networks are obtained.Finally,we give a numerical example to illustrate the correctness and effectiveness of our results.

关 键 词：时滞 模糊 M矩阵 细胞神经网络 输入对状态稳定 

分 类 号：O110.87[理学—数学]