文献类型：期刊文献

英文题名：A Dynamic Event-Triggered Approach to State Estimation for Switched Memristive Neural Networks With Nonhomogeneous Sojourn Probabilities

作者：Cheng, Jun[1,2];Liang, Lidan[3];Park, Ju H.[4];Yan, Huaicheng[5];Li, Kezan[6]

机构：[1]Guangxi Normal Univ, Sch Math & Stat, Ctr Appl Math Guangxi, Guilin 541006, Peoples R China;[2]Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Sichuan, Peoples R China;[3]Guangxi Normal Univ, Sch Math & Stat, Guilin 541006, Peoples R China;[4]Yeungnam Univ, Dept Elect Engn, Kyongsan 38541, South Korea;[5]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[6]Guilin Univ Elect Technol, Sch Math & Comp Sci, Guangxi Key Lab Cryptog & Informat Secur, Guilin 541004, Peoples R China

年份：2021

卷号：68

期号：12

起止页码：4924

外文期刊名：IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS

收录：;EI(收录号：20214311047650);WOS:【SCI-EXPANDED(收录号:WOS:000724482800013)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 12161011 and Grant 62173100, in part by the National Natural Science Foundation of Guangxi Province under Grant 2020GXNSFAA159049 and Grant 2020GXNSFFA297003, in part by Guangxi Science and Technology Base and Specialized Talents under Grant Guike AD20159057, and in part by the Training Program for 1000 Young and Middle-Aged Cadre Teachers in Universities of Guangxi Province. The work of Ju H. Park was supported by the National Research Foundation of Korea (NRF) Grant by the Korean Government (Ministry of Science and ICT) under Grant 2019R1A5A8080290.

语种：英文

外文关键词：Switches; Markov processes; State estimation; Switched systems; Memristors; Market research; Dynamical systems; Switched memristive neural networks; nonhomogeneous sojourn probabilities; average dwell time; dynamic event-triggered approach

摘要：This paper investigates the state estimation for switched memristive neural networks with nonhomogeneous sojourn probabilities. Essentially different from most current literature, a novel switching law is developed to depict the dynamic behavior of switched memristive neural networks, in which the sojourn probabilities of each subsystem are assumed to be nonhomogeneous, and a higher-level deterministic switching signal is proposed to regulate proper feedback switching information by means of the average dwell time approach. Meanwhile, to alleviate the constraint network bandwidth resource efficiently, a dynamic event-triggered mechanism with a novel threshold parameter is proposed in determining if the current data should be released or not. By resorting to the Lyapunov functional technique and the stochastic analysis strategy, some sufficient conditions are addressed to ensure the stochastic stability of the augmented switched memristive neural networks. In the end, the effectiveness and superiority of the developed results are verified by a numerical example.