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
Cognitive Neurodynamics
Erschienen in: Cognitive Neurodynamics 1/2014

01.02.2014 | Research Article

Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays

verfasst von: Zhichun Yang, Weisong Zhou, Tingwen Huang

Erschienen in: Cognitive Neurodynamics | Ausgabe 1/2014

Einloggen

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

search-config
loading …

Abstract

In this paper, input-to-state stability problems for a class of recurrent neural networks model with multiple time-varying delays are concerned with. By utilizing the Lyapunov–Krasovskii functional method and linear matrix inequalities techniques, some sufficient conditions ensuring the exponential input-to-state stability of delayed network systems are firstly obtained. Two numerical examples and its simulations are given to illustrate the efficiency of the derived results.
Vorheriger Artikel A modular latching chain
Nächster Artikel A linear model for characterization of synchronization frequencies of neural networks

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
Ahn CK (2010a) An H∞ approach to stability analysis of switched Hopfield neural networks with time-delay. Nonlinear Dyn 60(4): 703–711CrossRef
Ahn CK (2010b) Passive learning and input-to-state stability of switched Hopfiled neural networks with time-delay. Inf Sci 180: 4582–4594CrossRef
Ahn CK (2010c) \(L_2-L_\infty\) nonlinear system identification via recurrent neural networks. Nonlinear Dyn 62:543–552
Ahn CK (2011a) Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks. Fuzzy Sets Syst 179: 100–111CrossRef
Ahn CK (2011b) Robust stability of recurrent neural networks with ISS learning algorithm. Nonliear Dyn 65:413–419CrossRef
Ahn CK (2012a) Switched exponential state estimation of neural networks based on passivity theory. Nonlinear Dyn 67(1): 573–586CrossRef
Ahn CK (2012b) Linear matrix inequality optimization approach to exponential robust filtering for switched Hopfield neural networks. J Optim Theory Appl 154 (2): 573–587CrossRef
Ahn CK (2012c) An error passivation approach to filtering for switched neural networks with noise disturbance. Neural Comput Appl 21 (5): 853–861CrossRef
Angeli D, Sontag ED, Wang Y (2000) A characterization of integral input to state stability. IEEE Trans Automat Contr 45: 1082–1097CrossRef
Arik S (2000) Stability analysis of delayed neural networks. IEEE Trans Cricuits Syst I: Fund Theory Appl 47: 1089–1092CrossRef
Ensari T, Arik S (2005) Global stability analysis of neural networks with multiple time-varying delays. IEEE Trans Autom Contr 50: 1781–1785CrossRef
Grujiá LT, Michel AN (1991) Exponential stability and trajectory bands of neural networks under structural variations. IEEE Trans Cricuits Syst 38: 1182–1192CrossRef
Hong YG, Jiang ZP, Feng G(2010) Finite-time input-to-state stability and applications to finite-time control design. SIAM J Contr Optim 48: 4395–4418CrossRef
Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two state neurons. Proc Natl Acad Sci USA 81: 3088–3092PubMedCrossRef
Hopfield JJ, Tank DW (1986) Computing with neural networks: a model. Science (New Series) 233: 625–633CrossRef
Horn RA, Johnson CR (1991) Topics in matrix analysis. Cambridge University Press, LondonCrossRef
Huang H, Feng G (2009) Delay-dependent H∞ and generalized filtering H 2 for delayed neural networks. IEEE Trans Circuits Syst I: Reg Pap 56(4): 846–857CrossRef
Huang T, Li C, Duan S, Starzyk J (2012) Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects. IEEE Trans Neural Netw Learn Syst 23: 866–875CrossRef
Jiang ZP, Wang Y (2001) Input-to-state stability for discrete-time nonliear systems. Automatica 37: 857-89CrossRef
Liu X, Cao J (2010) Robust state estimation for neural networks with discontinuous activations. IEEE Trans Syst Man Cybern Part B Cybern 40(6): 1083–4419
Matsouka K (1992) Stability conditions for nonlinear continuous neural networks with asymmetric connections weights. Neural Netw 5: 495–500CrossRef
Sanchez EN, Perez JP (1999) Input-to-state stability (ISS) analysis for dynamical neural networks. IEEE Trans Circuits Syst I: Fundam Theorem Appl 46: 1395–1398CrossRef
Sontag ED (1989) Smooth stabilition implies corprime factorization. IEEE Trans Autom Contr 34: 435–443CrossRef
Sontag ED (1990) Further facts about input-to-state stabilization. IEEE Trans Autom Contr 35: 473–476CrossRef
Sontag ED, Wang Y (1995) On characterizations of the input-to-state stability property. Syst Contr Lett 24: 351–359CrossRef
Wu H, Feng W, Liang X (2008) New stability criteria for uncertain neural networks with interval time-varying delays. Cogn Neurodyn 2(4): 363–370PubMedCentralPubMedCrossRef
Wu X, Wang Y, Huang L, Zuo Y (2010) Robust stability analysis of delayed Takagi-Sugeno fuzzy Hopfield neural networks with discontinuous activation functions. Cogn Neurodyn 4(4): 347–354PubMedCentralPubMedCrossRef
Yang ZC, Hong YG (2012a) Stabilization of impulsive hybrid systems using quantized input and output feedback. Asian J Contr 14: 679–692CrossRef
Yang ZC, Zhou WS (2012b) Input-to-state stability of impulsive hybrid systems with stochastic effects. In Proceedings of the 24th IEEE China conference on decision and control pp 286–291
Zhang HG, Wang ZS, Liu DR (2008) Global asymptotic stability of recurrent neural networks with multiple time-varying delays. IEEE Trans Neural Netw 19: 855–873PubMedCrossRef
Zhu S, Shen Y (2012) Two algebraic criteria for input-to-state stability of recurrent neural networks with time-varying delays. Neural Comput Appl. doi 1007/s00521-012-0882-9
Metadaten
Titel
Exponential input-to-state stability of recurrent neural networks with multiple time-varying delays
verfasst von
Zhichun Yang
Weisong Zhou
Tingwen Huang
Publikationsdatum
01.02.2014
Verlag
Springer Netherlands
Erschienen in
Cognitive Neurodynamics / Ausgabe 1/2014
Print ISSN: 1871-4080
Elektronische ISSN: 1871-4099
DOI
https://doi.org/10.1007/s11571-013-9258-9

Weitere Artikel der Ausgabe 1/2014

Cognitive Neurodynamics 1/2014 Zur Ausgabe

Review Paper

Time domain measures of inter-channel EEG correlations: a comparison of linear, nonparametric and nonlinear measures

Brief Communication

Vague-to-crisp dynamics of percept formation modeled as operant (selectionist) process

Research Article

A linear model for characterization of synchronization frequencies of neural networks

Research Article

Visual pattern discrimination by population retinal ganglion cells’ activities during natural movie stimulation

Brief Communication

Signal integration on the dendrites of a pyramidal neuron model

Research Article

Information flow in heterogeneously interacting systems

Neuer Inhalt

    Bildnachweise