Further results on finite-time synchronization of delayed inertial memristive neural networks via a novel analysis method

被引：73

作者：

Hua, Lanfeng ^{[1
]}

Zhong, Shouming ^{[1
]}

Shi, Kaibo ^{[2
]}

Zhang, Xiaojun ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China

[2] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Sichuan, Peoples R China

来源：

NEURAL NETWORKS | 2020年 / 127卷

基金：

中国国家自然科学基金;

关键词：

Finite-time synchronization; Inertial memristive neural networks; New inequality methods; Mixed time-varying delays; VARYING DELAYS; STABILITY; DYNAMICS; PASSIVITY; CHAOS;

D O I：

10.1016/j.neunet.2020.04.009

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we propose a novel analysis method to investigate the finite-time synchronization (FTS) control problem of the drive-response inertial memristive neural networks (IMNNs) with mixed time-varying delays (MTVDs). Firstly, an improved control scheme is proposed under the delay-independent conditions, which can work even when the past state cannot be measured or the specific time delay function is unknown. Secondly, based on the assumption of bounded activation functions, we establish a new Lemma, which can effectively deal with the difficulties caused by memristive connection weights and MTVDs. Thirdly, by constructing a suitable Lyapunov functions and using a new inequality method, novel sufficient conditions to ensure the FTS for the discussed IMNNs are obtained. Compared with the existing results, our results obtained in a more general framework are more practical. Finally, some numerical simulations are given to substantiate the effectiveness of the theoretical results. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页码：47 / 57

页数：11

共 49 条

[1] Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication [J].

Alimi, Adel M. ;

Aouiti, Chaouki ;

Assali, El Abed .

NEUROCOMPUTING, 2019, 332 :29-43

[2] MODELS OF MEMBRANE RESONANCE IN PIGEON SEMICIRCULAR CANAL TYPE-II HAIR-CELLS [J].

ANGELAKI, DE ;

CORREIA, MJ .

BIOLOGICAL CYBERNETICS, 1991, 65 (01) :1-10

[3]

[Anonymous], 2020, NEUROCOMPUTING

[4]

[Anonymous], INT J CONTROL

[5] STABILITY AND DYNAMICS OF SIMPLE ELECTRONIC NEURAL NETWORKS WITH ADDED INERTIA [J].

BABCOCK, KL ;

WESTERVELT, RM .

PHYSICA D, 1986, 23 (1-3) :464-469

[6] DYNAMICS OF SIMPLE ELECTRONIC NEURAL NETWORKS [J].

BABCOCK, KL ;

WESTERVELT, RM .

PHYSICA D, 1987, 28 (03) :305-316

[7] Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors [J].

Cantley, Kurtis D. ;

Subramaniam, Anand ;

Stiegler, Harvey J. ;

Chapman, Richard A. ;

Vogel, Eric M. .

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2012, 23 (04) :565-573

[8] Fixed-time synchronization of inertial memristor-based neural networks with discrete delay [J].

Chen, Chuan ;

Li, Lixiang ;

Peng, Haipeng ;

Yang, Yixian .

NEURAL NETWORKS, 2019, 109 :81-89

[9] Finite-time synchronization of delayed fuzzy cellular neural networks with discontinuous activations [J].

Duan, Lian ;

Wei, Hui ;

Huang, Lihong .

FUZZY SETS AND SYSTEMS, 2019, 361 (56-70) :56-70

[10] Global exponential synchronization of inertial memristive neural networks with time-varying delay via nonlinear controller [J].

Gong, Shuqing ;

Yang, Shaofu ;

Guo, Zhenyuan ;

Huang, Tingwen .

NEURAL NETWORKS, 2018, 102 :138-148

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