STATE ESTIMATION OF MEMRISTOR-BASED STOCHASTIC NEURAL NETWORKS WITH MIXED VARIABLE DELAYS

被引：0
|
作者：
Saravanakumar, Ramasamy [1 ]
Dutta, Hemen [2 ]
机构：
[1] Hiroshima Univ, Grad Sch Adv Sci & Engn, 1-4-1 Kagamiyama, Higashihiroshima 7398527, Japan
[2] Gauhati Univ, Dept Math, Gauhati 781014, Assam, India
来源：
MISKOLC MATHEMATICAL NOTES | 2023年 / 24卷 / 03期
基金：
日本学术振兴会;
关键词：
distributed variable delay; memristor-based stochastic neural networks; quadruple integral; state estimation; EXPONENTIAL STABILITY; SYNCHRONIZATION; DISCRETE; PASSIVITY;
D O I：
10.18514/MMN.2023.4028
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper studies the state estimation problem for memristor-based stochastic neural networks (MSNNs) with mixed variable delays. A new Lyapunov-Krasovskii functional (LKF) with quadruple integral terms is incorporated. Then, asymptotic stability conditions are established for the error system using a linear matrix inequality technique. The estimator gain can be obtained by solving the linear matrix inequalities. Numerical simulations are given to demonstrate the effectiveness and superiority of the new scheme.
引用
收藏
页码：1495 / 1513
页数：19
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