文献类型：期刊文献

英文题名：Improved Stability Analysis Results of Generalized Neural Networks With Time-Varying Delays

作者：Zhai, Zhengliang[1];Yan, Huaicheng[2,3];Chen, Shiming[1];Zeng, Hongbing[4];Wang, Meng[5]

机构：[1]East China LiaoTong Univ, Sch Elect & Automat Engn, Nanchang 330000, Jiangxi, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China;[4]Hunan Univ Technol, Sch Elect & Informat Engn, Zhuzhou 412007, Peoples R China;[5]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：34

期号：11

起止页码：9404

外文期刊名：IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS

收录：;EI(收录号：20221712038487);WOS:【SCI-EXPANDED(收录号:WOS:000785743100001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62073143, Grant 61922063, and Grant 62003139, in part by the Program of Shanghai Academic Research Leader under Grant 19XD1421000, in part by the Shanghai and HongKong-Macao-Taiwan Science and Technology Cooperation Project under Grant 19510760200, in part by the Shanghai Shuguang Project under Grant 18SG18, and in part by the Innovation Program of Shanghai Municipal Education Commission under Grant 2021-01-07-00-02-E00107. (Corresponding author: Huaicheng Yan.)

语种：英文

外文关键词：Delays; Stability criteria; Linear matrix inequalities; Symmetric matrices; Numerical stability; Upper bound; Computational complexity; Generalized neural networks (GNNs); linear matrix inequalities (LMIs); matrix-valued cubic polynomials; time-varying delay

摘要：This article studies the stability problem of generalized neural networks (GNNs) with time-varying delay. The delay has two cases: the first case is that the delay's derivative has only upper bound, the other case has no information of its derivative or itself is not differentiable. For both two cases, we provide novel stability criteria based on novel Lyapunov-Krasovskii functionals (LKFs) and new negative definite conditions (NDCs) of matrix-valued cubic polynomials. In contrast with the existing methods, in this article, the proposed criteria do not need to introduce extra state variables, and the positive-definite constraint on the novel LKF is relaxed. Moreover, based on free-matrix-based inequality (FMBI) and new NDCs, the stability conditions are expressed as linear matrix inequalities (LMIs). Eventually, the merits and efficiency of the proposed criteria are checked through some classical numerical examples.