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 Processing Letters
Erschienen in: Neural Processing Letters 2/2019

29.11.2018

Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed Delays

verfasst von: Yanjun Liu, Yu Qin, Junjian Huang, Tingwen Huang, Xinbo Yang

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

Einloggen

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

search-config
loading …

Abstract

This paper investigates finite-time synchronization of complexed-valued neural networks with multiple time-varying delays and infinite distributed delays. By separating the complex-valued neural networks into the real and the imaginary parts, the corresponding equivalent real-valued systems are obtained. Some sufficient conditions are derived for finite-time synchronization of the drive-response system based on the new Lyapunov–Krasovskii function and the new analysis techniques. Numerical examples demonstrate the effectiveness of the theoretical results.
Vorheriger Artikel On the Flexible Dynamics Analysis for the Unified Discrete-Time RNNs
Nächster Artikel Several Novel Dynamic Ensemble Selection Algorithms for Time Series Prediction

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

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

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"

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
Literatur
1.
Lee D (2006) Improvement of complex-valued Hopfield associative memory by using generalized projection rules. IEEE Trans Neural Netw 17(5):1341–1347CrossRef
2.
Zhou W, Zurada J (2009) Discrete-time recurrent neural networks with complex-valued linear threshold neurons. IEEE Trans Circuits Syst II 56(8):669–673CrossRef
3.
Hirose A, Yoshida S (2012) Generalization characteristics of complexvalued feedforward neural networks in relation to signal coherence. IEEE Trans Neural Netw Learn Syst 23(4):541–551CrossRef
4.
Dini D, Mandic D (2012) Class of widely linear complex Kalman filters. IEEE Trans Neural Netw Learn Syst 23(5):775–786CrossRef
5.
Zhou B, Song Q (2013) Boundedness and complete stability of complex-valued neural networks with time delay. IEEE Trans Neural Netw Learn Syst 24(8):1227–1238CrossRef
6.
Xu X, Zhang J, Shi J (2014) Exponential stability of complex-valued neural networks with mixed delays. Neurocomputing 128(128):483–490CrossRef
7.
Xu X, Zhang J, Shi J (2017) Dynamical behaviour analysis of delayed complex-valued neural networks with impulsive effect. Int J Syst Sci 48(4):686–694MathSciNetCrossRef
8.
9.
10.
Tan Z, Ali M (2001) Associative memory using synchronization in a chaotic neural network. Int J Mod Phys C 12(1):19–29CrossRef
11.
Hoppensteadt F, Izhikevich E (2002) Pattern recognition via synchronization in phase-locked loop neural networks. IEEE Trans Neural Netw 11(3):734–738CrossRef
12.
Park H (2006) Chaos synchronization between two different chaotic dynamical systems. Chaos Solitons Fractals 27(2):549–554CrossRef
13.
Bowong S, Kakmeni F, Fotsin H (2006) A new adaptive observer-based synchronization scheme for private communication. Phys Lett A 355(3):193–201CrossRef
14.
Sheng L, Yang H (2008) Exponential synchronization of a class of neural networks with mixed time-varying delays and impulsive effects. Neurocomputing 71(16):3666–3674CrossRef
15.
Song Q (2009) Design of controller on synchronization of chaotic neural networks with mixed time-varying delays. Neurocomputing 72(13):3288–3295CrossRef
16.
Wang Z, Zhang H (2013) Synchronization stability in complex interconnected neural networks with nonsymmetric coupling. Neurocomputing 108(5):84–92CrossRef
17.
Shen J, Cao J (2011) Finite-time synchronization of coupled neural networks via discontinuous controllers. Cogn Neurodyn 5(4):373–385CrossRef
18.
Yang X, Cao J (2010) Finite-time stochastic synchronization of complex networks. Appl Math Model 34(11):3631–3641MathSciNetCrossRef
19.
Mei J, Jiang M, Wang X, Han J, Wang S (2014) Finite-time synchronization of drive-response systems via periodically intermittent adaptive control. J Frankl Inst 351(5):2691–2710MathSciNetCrossRef
20.
Fei Y, Mei J, Wu Z (2016) Finite-time synchronisation of neural networks with discrete and distributed delays via periodically intermittent memory feedback control. IET Control Theory Appl 10(10):1630–1640MathSciNet
21.
Huang J, Li C, Huang T, He X (2014) Finite-time lag synchronization of delayed neural networks. Neurocomputing 139(13):145–149CrossRef
22.
Hu C, Yu J, Jiang H (2014) Finite-time synchronization of delayed neural networks with Cohen–Grossberg type based on delayed feedback control. Neurocomputing 143(16):90–96CrossRef
23.
Shen H, Park J, Wu Z, Zhang Z (2015) Finite-time \(H^{\infty }\) synchronization for complex networks with semi-Markov jump topology. Commun Nonlinear Sci Numer Simul 24(1–3):40–51MathSciNetCrossRef
24.
Shen H, Ju H, Wu Z (2014) Finite-time synchronization control for uncertain Markov jump neural networks with input constraints. Nonlinear Dyn 77(4):1709–1720MathSciNetCrossRef
25.
Yang X (2014) Can neural network swith arbitrary delays be finite-timely synchronized. Neurocomputing 143(16):275–281CrossRef
26.
Shi L, Yang X, Li Y, Feng Z (2016) Finite-time synchronization of nonidentical chaotic systems with multiple time-varying delays and bounded perturbations. Nonlinear Dyn 83(1):75–87MathSciNetCrossRef
27.
Zhou C, Zhang W, Yang X, Xu C, Feng J (2017) Finite-time synchronization of complex-valued neural networks with mixed delays and uncertain perturbations. Neural Process Lett 1(4):1–21
Metadaten
Titel
Finite-Time Synchronization of Complex-Valued Neural Networks with Multiple Time-Varying Delays and Infinite Distributed Delays
verfasst von
Yanjun Liu
Yu Qin
Junjian Huang
Tingwen Huang
Xinbo Yang
Publikationsdatum
29.11.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9958-6

Weitere Artikel der Ausgabe 2/2019

Neural Processing Letters 2/2019 Zur Ausgabe

OriginalPaper

Multi-view Intact Discriminant Space Learning for Image Classification

OriginalPaper

Competitive Cross-Entropy Loss: A Study on Training Single-Layer Neural Networks for Solving Nonlinearly Separable Classification Problems

OriginalPaper

Improved SMOTE Algorithm to Deal with Imbalanced Activity Classes in Smart Homes

OriginalPaper

Discriminative Graph Based Similarity Boosting

OriginalPaper

Solving Partial Differential Equation Based on Bernstein Neural Network and Extreme Learning Machine Algorithm

OriginalPaper

An Artificial Neural Network Model to Solve the Fuzzy Shortest Path Problem

Neuer Inhalt

    Bildnachweise