Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Processing Letters
Published in: Neural Processing Letters 2/2021

05-03-2021

Prescribed-Time Synchronization of Coupled Memristive Neural Networks with Heterogeneous Impulsive Effects

Authors: Yuangui Bao, Yijun Zhang, Baoyong Zhang, Yu Guo

Published in: Neural Processing Letters | Issue 2/2021

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

This paper is concerned with the prescribed-time synchronization of coupled memristive neural networks (MNNs). The impulsive effects with heterogeneous impulsive instants and impulsive strengths are considered. Different from related results on the fixed-time synchronization, this paper focuses on the prescribed-time synchronization of coupled MNNs, in which the settling time can be prescribed according to task requirements. A sufficient criterion is derived to ensure the prescribed-time synchronization of coupled MNNs. Besides, the proposed design method provides less conservatism compared with the existing results. A numerical example and an application in secure communication are provided to show the effectiveness of the theoretical results
previous article Kernelized Linear Autoencoder
next article Deep Neural Network to Predict Answer Votes on Community Question Answering Sites

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Liu H, Wang Z, Shen B, Huang T, Fuad AE (2018) Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays. Neural Netw 102:1–9CrossRef
2.
Zhang G, Zeng Z (2018) Exponential stability for a class of memristive neural networks with mixed time-varying delays. Appl Math Comput 321:544–554MathSciNetMATH
3.
Zhu S, Ye E, Liu D, Zhou S (2018) New algebraic criteria for global exponential periodicity and stability of memristive neural networks with variable delays. Neural Process Lett 48:1749–1766CrossRef
4.
Gong S, Yang S, Guo Z, Huang T (2019) Global exponential synchronization of memristive competitive neural networks with time-varying delay via nonlinear control. Neural Process Lett 49:103–119CrossRef
5.
6.
Zhang Y, Bao Y (2020) Event-triggered hybrid impulsive control for synchronization of memristive neural networks. Sci China Inf Sci 63:150206MathSciNetCrossRef
7.
Ding S, Wang Z, Huang Z, Zhang H (2017) Novel switching jumps dependent exponential synchronization criteria for memristor-based neural networks. Neural Process Lett 45:15–28CrossRef
8.
Li R, Gao X, Cao J (2019) Quasi-state estimation and quasi-synchronization control of quaternion-valued fractional-order fuzzy memristive neural networks: vector ordering approach. Appl Math Comput 362:124572MathSciNetMATH
9.
We F, Chen G, Wang W (2020) Finite-time synchronization of memristor neural networks via interval matrix method. Neural Netw 127:7–18CrossRef
10.
Zhang Y, Li L, Peng H, Xiao J, Yang Y, Zheng M, Zhao H (2018) Finite-time synchronization for memristor-based BAM neural networks with stochastic perturbations and time-varying delays. Int J Robust Nonlinear Control 28:5118–5139MathSciNetCrossRef
11.
12.
Polyakov A (2012) Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans Autom Control 57(8):2106–2110MathSciNetCrossRef
13.
Li R, Cao J, Alsaedi A, Alsaadi F (2017) Exponential and fixed-time synchronization of Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms. Appl Math Comput 313:37–51MathSciNetCrossRef
14.
Khanzadeh A, Pourgholi M (2018) Fixed-time leader–follower consensus tracking of second-order multi-agent systems with bounded input uncertainties using non-singular terminal sliding mode technique. IET Control Theory Appl 12(5):679–686MathSciNetCrossRef
15.
Li P, Song Z, Wang Z, Liu W (2020) Fixed-time consensus for disturbed multiple Euler–Lagrange systems with connectivity preservation and quantized input. Appl Math Comput 380:125303MathSciNetCrossRef
16.
Lü H, He W, Han Q-L, Peng C (2018) Fixed-time synchronization for coupled delayed neural networks with discontinuous or continuous activations. Neurocomputing 314:143–153CrossRef
17.
Zheng M, Li L, Peng H, Xiao J, Yang Y, Zhang Y, Zhao H (2018) Fixed-time synchronization of memristor-based fuzzy cellular neural network with time-varying delay. J Frankl Inst 355:6780–6809MathSciNetCrossRef
18.
Wei R, Cao J (2019) Fixed-time synchronization of quaternion-valued memristive neural networks with time delays. Neural Netw 113:1–10CrossRef
19.
20.
Li R, Cao J (2018) Finite-time and fixed-time stabilization control of delayed memristive neural networks: robust analysis technique. Neural Process Lett 47:1077–1096CrossRef
21.
Zhang Y, Zhuang J, Xia Y, Bai Y, Cao J, Gu L (2019) Fixed-time synchronization of the impulsive memristor-based neural networks. Commun Nonlinear Sci Numer Simul 77:40–53MathSciNetCrossRef
22.
Wang G, Shen Y (2014) Exponential synchronization of coupled memristive neural networks with time delays. Neural Comput Appl 24:1421–1430CrossRef
23.
Li J, Jiang H, Hu C, Alsaedi A (2019) Finite/fixed-time synchronization control of coupled memristive neural networks. J Frankl Inst 356(16):9928–9952MathSciNetCrossRef
24.
Yang C, Huang L, Cai Z (2019) Fixed-time synchronization of coupled memristor-based neural networks with time-varying delays. Neural Netw 116:101–109CrossRef
25.
26.
Zhang W, Tang Y, Wu J, Xiaotai F (2014) Synchronization of nonlinear dynamical networks with heterogeneous impulses. IEEE Trans Circuits Syst I Regul Pap 61(4):1220–1228CrossRef
27.
Zhang H, Zhang W, Miao Q, Cui Y (2019) Synchronization of switched coupled neural networks with distributed impulsive effects: an impulsive strength dependent approach. Neural Process Lett 50:515–529CrossRef
28.
Rakkiyappan R, Gayathri D, Velmurugan G, Cao J (2019) Exponential synchronization of inertial memristor-based neural networks with time delay using average impulsive interval approach. Neural Process Lett 50:2053–2071CrossRef
29.
Li L, Mu G (2019) Synchronization of coupled complex-valued impulsive neural networks with time delays. Neural Process Lett 50:2515–2527CrossRef
30.
31.
Li Y (2017) Impulsive synchronization of stochastic neural networks via controlling partial states. Neural Process Lett 49:59–69CrossRef
32.
Li Y, Lou J, Wang Z, Alsaadi FE (2018) Synchronization of dynamical networks with nonlinearly coupling function under hybrid pinning impulsive controllers. J Frankl Inst 355:6520–6530MathSciNetCrossRef
33.
Ji X, Lu J, Jianquan L, Qiu J, Kaibo S (2020) A unified criterion for global exponential stability of quaternion-valued neural networks with hybrid impulses. Int J Robust Nonlinear Control 30:8098–8116MathSciNetCrossRef
34.
Yang X, Lam J, Ho DWC, Feng Z (2017) Fixed-time synchronization of complex networks with impulsive effects via nonchattering control. IEEE Trans Autom Control 62(11):5511–5521MathSciNetCrossRef
35.
Li N, Wu Q, Xiaoqun Y (2020) Fixed-time synchronization of complex dynamical network with impulsive effects. IEEE Access 8:33072–33079CrossRef
36.
Li H, Li C, Huang T, Zhang W (2018) Fixed-time stabilization of impulsive Cohen–Grossberg BAM neural networks. Neural Netw 98:203–211CrossRef
37.
Jingting H, Sui G, Lv X, Li X (2018) Fixed-time control of delayed neural networks with impulsive perturbations. Nonlinear Anal Model Control 23(6):904–920MathSciNetCrossRef
38.
39.
Wang Y, Song Y, Hill DJ, Krstic M (2019) Prescribed-time consensus and containment control of networked multiagent systems. IEEE Trans Cybern 49(4):1138–1147CrossRef
40.
Liu X, Ho DWC, Xie C (2020) Prespecified-time cluster synchronization of complex networks via a smooth control approach. IEEE Trans Cybern 50(4):1771–1775CrossRef
41.
Wen S, Bao G, Zeng Z, Chen Y, Huang T (2013) Global exponential synchronization of memristor-based recurrent neural networks with time-varying delays. Neural Netw 48:195–203CrossRef
42.
Filippov AF (1960) Differential equations with discontinuous righthand sides. Mate Sb 93(1):99–128
43.
Liu Y, Xu J, Pei L, Liang J (2016) Global stability of Clifford-valued recurrent neural networks with time delays. Nonlinear Dyn 84:767–777MathSciNetCrossRef
44.
Liu Y, Zheng Y, Lu J, Cao J, Rutkowski L (2020) Constrained quaternion-variable convex optimization: a quaternion-valued recurrent neural network approach. IEEE Trans Neural Netw Learn Syst 31(3):1022–1035MathSciNetCrossRef
45.
Zhang W, Zuo Z, Wang Y, Zhang Z (2020) Double-integrator dynamics for multiagent systems with antagonistic reciprocity. IEEE Trans Cybern 50(9):4110–4120CrossRef
46.
47.
Zhou Y, Zeng Z (2019) Event-triggered impulsive control on quasi-synchronization of memristive neural networks with time-varying delays. Neural Netw 110:55–65CrossRef
48.
Metadata
Title
Prescribed-Time Synchronization of Coupled Memristive Neural Networks with Heterogeneous Impulsive Effects
Authors
Yuangui Bao
Yijun Zhang
Baoyong Zhang
Yu Guo
Publication date
05-03-2021
Publisher
Springer US
Published in
Neural Processing Letters / Issue 2/2021
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10469-y

Other articles of this Issue 2/2021

Neural Processing Letters 2/2021 Go to the issue

OriginalPaper

Exponential Synchronization of Nonlinear Multi-weighted Complex Dynamic Networks with Hybrid Time Varying Delays

OriginalPaper

KTBoost: Combined Kernel and Tree Boosting

OriginalPaper

Symbolic Regression Based Extreme Learning Machine Models for System Identification

OriginalPaper

Estimating the Optimal Number of Clusters Via Internal Validity Index

OriginalPaper

Spatial Rough Intuitionistic Fuzzy C-Means Clustering for MRI Segmentation

OriginalPaper

Deep Neural Network to Predict Answer Votes on Community Question Answering Sites