Reachable set bounding for a class of memristive complex-valued neural networks with disturbances

被引：9

作者：

Gao, Yu ^{[1
]}

Zhu, Song ^{[1
]}

Li, Jinyu ^{[1
]}

机构：

[1] China Univ Min & Technol, Sch Math, Xuzhou 221116, Jiangsu, Peoples R China

来源：

NEUROCOMPUTING | 2020年 / 385卷 / 385期

关键词：

Complex-valued neural networks; Memristor; Reachable set; Bounded disturbances; GLOBAL EXPONENTIAL STABILITY; TIME-VARYING DELAYS; LINEAR-SYSTEMS; FINITE-TIME; SYNCHRONIZATION; STABILIZATION; CONVERGENCE; DEVICES;

D O I：

10.1016/j.neucom.2019.12.085

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This study focus on reachable set bounding for memristive complex-valued neural networks (MCVNNs) with bounded input disturbances. In order to find the region such that all the solutions of MCVNNs with bounded input disturbances converge within it, we utilize a approach based on Lyapunov-Krasovskii functional (LKF). From this, we derive some improved conditions to get the bounding ellipsoid in accordance with linear matrix inequalities (LMIs), the stability criteria are also obtained. At last, two illustrative simulations are also provided to show the availability and validity of obtained conclusions. (C) 2019 Elsevier B.V. All rights reserved.

引用

页码：368 / 377

页数：10

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