Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2021

05.08.2020 | Original Article

New finite-time synchronization of memristive Cohen–Grossberg neural network with reaction–diffusion term based on time-varying delay

verfasst von: Fangmin Ren, Minghui Jiang, Hao Xu, Xue Fang

Erschienen in: Neural Computing and Applications | Ausgabe 9/2021

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

This paper focuses on the finite-time synchronization of memristive Cohen–Grossberg neural networks with time delays based on the reaction–diffusion term. Two new finite-time synchronous lemmas, Lemmas 2.3 and 2.4, have been obtained through some integration techniques. Since the proposal of Lemma 2.5 solves the \({X^\varphi }\left( {u\left( t \right) } \right) \) problem in the denominator, and by designing two different controllers and inequality techniques, two finite-time synchronization theorems are finally obtained. Simulations are performed according to two examples to verify the validity of the results in this paper.
Vorheriger Artikel Joint deep separable convolution network and border regression reinforcement for object detection
Nächster Artikel Mobile neural intelligent information system based on edge computing with interactive data

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

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!

Jetzt informieren

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!

Jetzt informieren

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!

Jetzt informieren
Literatur
1.
Zhang H, Zeng Z (2019) Synchronization of multiple reaction–diffusion neural networks with heterogeneous and unbounded time-varying delays. IEEE Trans Cybern 49(8):2980–2991
2.
Wang D, Huang L (2018) Robust synchronization of discontinuous Cohen–Grossberg neural networks: Pinning control approach. J Frankl Inst 355:5866–5892MathSciNetMATH
3.
Mei J, Jiang M, Wang B, Liu Q (2014) Exponential p-synchronization of non-autonomous Cohen–Grossberg neural networks with reaction-diffusion terms via periodically intermittent control. Neural Process Lett 40:103–126
4.
Feng Y, Yang X, Song Q, Cao J (2018) Synchronization of memristive neural networks with mixed delays via quantized intermittent control. Appl Math Comput 339:874–887MathSciNetMATH
5.
Li R, Wei H (2016) Synchronization of delayed Markovian jump memristive neural networks with reaction–diffusion terms via sampled data control. Int J Mach Learn Cybern 7(1):157–169
6.
Wu H, Zhang X, Li R, Yao R (2015) Adaptive anti-synchronization and \(H_\infty \) anti-synchronization for memristive neural networks with mixed time delays and reaction–diffusion terms. Neurocomputing 168:726–740
7.
Tua Z, Ding N, Li L, Feng Y (2017) Adaptive synchronization of memristive neural networks with time-varying delays and reactionCdiffusion term. Appl Math Comput 311:118–128MathSciNet
8.
Zhang L, Yang Y, Xu X (2018) Synchronization analysis for fractional order memristive Cohen–Grossberg neural networks with state feedback and impulsive control. Phys A 506:644–660MathSciNet
9.
10.
Zhang Z, Li A, Yu S (2018) Finite-time synchronization for delayed complex-valued neural networks via integrating inequality method. Neurocomputing 318:248–260
11.
Zhang Z, Chen M, Li A (2020) Further study on finite-time synchronization for delayed inertial neural networks via inequality skills. Neurocomputing 373:15–23
12.
Zhang Z, Cao J (2019) Novel finite-time synchronization criteria for inertial neural networks with time delays via integral inequality method. IEEE Trans Neural Netw Learn Syst 30(5):1476–85MathSciNet
13.
Jia Q, Han Z, Tang W (2019) Synchronization of multi-agent systems with time-varying control and delayed communications. IEEE Trans Circuits Syst I Regul Pap 66(11):4429–38MathSciNet
14.
Cohen M (1983) Absolute stability of global pattern formation and parallel memory storage by competitive neural networks. IEEE Trans Syst Man Cybern 13:815–826MathSciNetMATH
15.
Yang X, Cao J, Yu W (2014) Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays. Cogn Neurodyn 8(3):239–249
16.
Ke L, Li W (2019) Exponential synchronization in inertial Cohen–Grossberg neural networks with time delays. J Frankl Inst 356:11285–11304MathSciNetMATH
17.
Lv T, Yan P (2010) Exponential synchronization of delayed fuzzy Cohen–Grossberg neural networks with reaction diffusion term. Lect Notes Comput Sci 6319:57–63
18.
Aouiti C, Assali E, Foutayeni Y (2019) Finite-time and fixed-time synchronization of inertial Cohen–Grossberg-type neural networks with time varying delays. Neural Process Lett 50:2407–2436
19.
Kong K, Zhu Q, Liang F, Nieto J (2019) Robust fixed-time synchronization of discontinuous Cohen-Grossberg neural networks with mixed time delays. Nonlinear Anal Model Control 24(4):603–625MathSciNetMATH
20.
Abdurahman A, Jiang H, Hu C (2017) General decay synchronization of memristor-based Cohen–Grossberg with mixed time-delays and discontinuous activations. J Frankl Inst-Eng Appl Math 354(15):7028–7052MathSciNetMATH
21.
Wei R, Cao J, Alsaedi A (2018) Fixed-time synchronization of memristive Cohen–Grossberg neural networks with impulsive effects. Int J Control Autom Syst 16(5):2214–2224
22.
Chua M (1971) Memristor-the missing circuit element. IEEE Trans Circuit Theory 18(5):507–519
23.
Strukov D, Snider G, Stewart G, Williams R (2008) The missing memristor found. Nature 453:80–83
24.
Liu Y, Liao X, Li C (2019) Exponential lag synchronization of memristive neural networks with reaction diffusion terms via neural activation function control and fuzzy model. Asian J Control 21(6):1–16MathSciNet
25.
26.
Liu D, Zhu S, Sun K (2019) Global anti-synchronization of complex-valued memristive neural networks with time delays. IEEE Trans Cybern 49:1735–1747
27.
Yang Z, Luo B, Liu D, Li Y (2017) Adaptive synchronization of delayed memristive neural networks with unknown parameters. IEEE Trans Syst Man Cybern Systems 9:1–11
28.
Chen L, Cao J, Wu R, Machado J, Lopes AM, Yang H (2017) Stability and synchronization of fractional-order memristive neural networks with multiple delays. Neural Netw 94:76–85MATH
29.
Wei R, Cao J (2019) Fixed-time synchronization of quaternion-valued memristive neural networks with time delays. Neural Netw 113:1–10MATH
30.
Yi C, Xu C, Feng J, Wang J, Zhao Y (2019) Pinning synchronization for reaction–diffusion neural networks with delays by mixed impulsive control. Neurocomput 339:270–278
31.
Monlay E, Perruquetti W (2006) Finite time stability and stabilization of a class of continuous systems. J Math Anal Appl 323(2):1430–1443MathSciNetMATH
32.
Bhat S, Bernstein D (2000) Finite-time stability of continuous autonomous systems. SIAM J Control Optim 38(3):751–766MathSciNetMATH
33.
Monlay E and Perruquetti W (2003) Finite time stability of non linear systems. In: 42nd IEEE conference on decision and control, vols 1–6, pp 3641–3646
34.
Polyakov A (2012) Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans Autom Control 57(8):2106–2110MathSciNetMATH
35.
Shen Y, Xia X (2008) Semi-global finite-time observers for nonlinear systems. Automatica 44:3152–3156MathSciNetMATH
36.
Miao P, Shen Y, Huang Y, Wang Y (2015) Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking. Neural Comput Appl 26:693–703
37.
Ji G, Hu C, Yu J, Jiang H (2018) Finite-time and fixed-time synchronization of discontinuous complex networks: a unified control framework design. J Frankl Inst 355:4665–4685MathSciNetMATH
38.
Li J, Jianga H, Hua C, Alsaedi A (2019) Finite/fixed-time synchronization control of coupled memristive neural networks. J Frankl Inst 356(16):9928–9952MathSciNetMATH
39.
Chen C, Li L, Peng H, Yang Y (2019) A new fixed-time stability theorem and its application to the synchronization control of memristive neural networks. Neurocomputing 349:290–300
40.
Hu C, Yu J, Chen Z, Jiang H (2017) Fixed-time stability of dynamical systems and fixed-time synchronization of coupled discontinuous neural networks. Neural Netw 89:74–83MATH
41.
Peng D, Li X, Aouiti C, Miaadi F (2018) Finite-time synchronization for Cohen–Grossberg neural networks with mixed time-delays. Neurocomputing 294:39–47
42.
Lu J (2008) Global exponential stability and periodicity of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Chaos Solitons Fractals 35(1):116–125MathSciNetMATH
43.
Hardy G, Littlewood J, Polya G (1952) Inequalities, 2nd edn. Cambridge University Press, CambridgeMATH
44.
Yoshizawa T (1966) Stability theory by Lyapunov’s second method. The Mathematical Society of Japan, TokyoMATH
45.
Metadaten
Titel
New finite-time synchronization of memristive Cohen–Grossberg neural network with reaction–diffusion term based on time-varying delay
verfasst von
Fangmin Ren
Minghui Jiang
Hao Xu
Xue Fang
Publikationsdatum
05.08.2020
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2021
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05259-x

Weitere Artikel der Ausgabe 9/2021

Neural Computing and Applications 9/2021 Zur Ausgabe

Original Article

A comparative study of pre-screening strategies within a surrogate-assisted multi-objective algorithm framework for computationally expensive problems

S.I.: SPIoT 2020

Overflow warning and remote monitoring technology based on improved random forest

S.I. : SPIoT 2020

Farmers credit optimization decision model and applications based on common risk guarantee fund

S.I. : SPIoT 2020

Credit system of smart logistics public information platform based on improved neural network

S.I.: SPIoT 2020

Early warning of enterprise finance risk of big data mining in internet of things based on fuzzy association rules

Original Article

Selecting data adaptive learner from multiple deep learners using Bayesian networks