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 3/2015

01.12.2015

Adaptive Finite-Time Complete Periodic Synchronization of Memristive Neural Networks with Time Delays

verfasst von: Huaiqin Wu, Ruoxia Li, Xiaowei Zhang, Rong Yao

Erschienen in: Neural Processing Letters | Ausgabe 3/2015

Einloggen

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

search-config
loading …

Abstract

This paper is concerned with the adaptive finite-time complete periodic synchronization issue for memristive based neural networks with time delays. Under the framework of Filippov solutions of the differential equations with discontinuous right-hand side, based on Mawhin-like coincidence theorem in set-valued analysis theory, the existence of periodic solution is proved. By applying Lyapunov–Krasovskii functional approach, adaptive controller is designed and unknown control parameters are determined by adaptive update law. A novel and useful finite-time complete synchronization condition is obtained in terms of linear matrix inequalities to ensure the synchronization goal. An illustrative example is given to demonstrate the effectiveness of the theoretical results.
Vorheriger Artikel Bifurcation Behavior for an Electronic Neural Network Model with Two Different Delays
Nächster Artikel A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

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.
Chua LO (1971) Memristor-the missing circut element. IEEE Trans Circuit Theory 18:507–519CrossRef
2.
Strukov DB, Snider GS, Stewart DR, Williams RS (2008) The missing memristor found. Nature 453:80–83CrossRef
3.
Tour JM, He T (2008) Electronics: the fourth element. Nature 453:42–43CrossRef
4.
5.
Pershin YV, Ventra MD (2008) Spin memristive system: spin memory effects in semiconductor spintronics. Phys Rev B 78:1–4
6.
Yang J, Pickett MD, Li X, Ohlberg DAA, Stewart DR, Williams RS (2008) Memristive switching mechanism for metal/oxide/metal nanodevices. Nat Nanotechnol 3:429–433CrossRef
7.
Wang X, Chen Y, Xi H, Li H, Dimitrov D (2009) Spintronic memristor through spin-torque-induced magenetization motion. IEEE Electron Device Lett 30:294–297CrossRef
8.
Merrikh-Bayat F, Shouraki SB (2011) Memristor-based circuits for performing basic arithmetic operations. Procedia Comput Sci 3:128–132CrossRef
9.
Riaza R (2010) Nondegeneracy conditions for active memristive circuits. IEEE Trans Circuits Syst II 57:223–227CrossRef
10.
Pershin YV, Ventra DM (2011) Memory effects in complex materials and nanoscale systems. Adv Phys 60:145–227CrossRef
11.
Corinto F, Ascoli A, Gilli M (2011) Nonlinear dynamics of memristor oscillators. IEEE Trans Circuits Syst I 58:1323–1336MathSciNetCrossRef
12.
Itoh M, Chua LO (2009) Memristor cellular automata and memristor discrete-time cellular neural networks. Int J Bifurc Chaos 19:3605–3656MathSciNetCrossRefMATH
13.
Pershin YV, DiVentra M (2010) xperimental demonstration of associative memory with memristive neural networks. Neural Netw 23:881C886CrossRef
14.
Wu A, Zeng Z (2012) Exponential stabilization of memristive neural networks with time delays. IEEE Trans Neural Netw 23:1919–1929CrossRef
15.
Wen S, Zeng Z, Huang T (2012) Exponential stability analysis of memristor-based recurrent neural networks with time-varying delays. Neurocomputing 97:233–240CrossRef
16.
Wen S, Zeng Z (2012) Dynamics analysis of a class of memristor-based recurrent networks with time-varying delays in the presence of strong external stimuli. Neural Process Lett 35:47–59CrossRef
17.
Wu A, Zeng Z (2012) Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays. Neural Netw 36:1–10CrossRefMATH
18.
Zhang G, Shen Y, Sun J (2012) Global exponential stability of a class of memristor-based recurrent neural networks with time-varying delays. Neurocomputing 97:149–154CrossRef
19.
20.
Zhang G, Shen Y (2013) Global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays. Inf Sci 232:386–396MathSciNetCrossRefMATH
21.
Wu H, Zhang L (2013) Almost periodic solution for memristive neural networks with time-varying delays. J Appl Math. doi: 10.​1155/​716172
22.
Strogatz SH, Stewart I (1993) Coupled oscillators and biological synchronization. Sci Am 269:102–109CrossRef
23.
Dimassi H, ALora A, Belghith S (2012) A new secured transmission scheme based on chaotic synchronization via smooth adaptive unknown-input observers. Commun Nonlinear Sci Numer Simul 17:3727–3739CrossRefMATH
24.
Chen D, Zhang R, Ma X, Liu S (2012) Chaotic synchronization and anti-synchronization for a novel class of multiple chaotic systems via a sliding mode control scheme. Nonlinear Dyn 69:35–55MathSciNetCrossRefMATH
25.
Yang X, Cao J, Lu J (2011) Synchronization of delayed complex dynamical networks with impulsive and stochastic effects. Nonlinear Anal 12:2252–2266MathSciNetCrossRefMATH
26.
Zhan M, Wei G, Lai C (2002) Transition from intermittency to periodicity in lag synchronization in coupled Rössler oscillators. Phys Rev E 65:036–040CrossRef
27.
28.
Zhang D, Xu J (2010) Projective synchronization of different chaotic time-delayed neural networks based on integral sliding mode controller. Appl Math Comput 217:164–174MathSciNetCrossRefMATH
29.
Suddheer KS, Sabir M (2009) Adaptive function projective synchronization of two-cell Quantum-CNN chaotic oscillators with uncertain parameters. Phys Lett A 373:1847–1851CrossRef
30.
Li G (2007) Modified projective synchronization of chaotic system. Chaos Solitons Fract 32:1786–1790CrossRefMATH
31.
Huang X, Lin W, Yang B (2005) Global finite-time synchronization of a class of uncertain nonlinear systems. Automatica 41:881–888MathSciNetCrossRefMATH
32.
33.
Wu A, Wen S, Zeng Z, Zhu X, Zhang J (2011) Exponential synchronization of memristor-based recurrent neural networks with time delays. Neurocomputing 74:3043–3050CrossRef
34.
35.
Wu A, Zeng Z (2013) Anti-synchronization control of a class of memristive recurrent neural networks. Commun Nonlinear Sci Numer Simul 18:373–385MathSciNetCrossRefMATH
36.
Wen S, Zeng Z, Huang T (2012) Adaptive synchronization of memristor-based Chua’s circuits. Phys Lett A 376:2775–2780CrossRef
37.
Aubin J, Frankowska H (1990) Set-valued analysis. Birkhauser, BostonMATH
38.
Filippov A (1984) Differential equations with discontinuous right-hand side., Mathematics and Its Applications (Soviet Series). Kluwer Academic, Boston
39.
Berman A, Plemmons R (1979) Nonnegative matrices in the mathematical science. Academic press, New York
40.
Clarke FH (1983) Optimization and non-smooth analysis. Wiley, New York
41.
Li Y, Lin Z (1995) Periodic solutions of differential inclusions. Nonlinear Anal Theory Methods Appl 24:631–641CrossRefMATH
42.
Forti M, Grazzini M, Nistri P, Pancioni L (2006) Generalized Lyapunov approach for convergence of neural networks with discontinuous or non-Lipschitz activations. Phys D Nonlinear Phenom 214:88–99MathSciNetCrossRefMATH
Metadaten
Titel
Adaptive Finite-Time Complete Periodic Synchronization of Memristive Neural Networks with Time Delays
verfasst von
Huaiqin Wu
Ruoxia Li
Xiaowei Zhang
Rong Yao
Publikationsdatum
01.12.2015
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2015
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9373-6

Weitere Artikel der Ausgabe 3/2015

Neural Processing Letters 3/2015 Zur Ausgabe

OriginalPaper

A Truly Online Learning Algorithm using Hybrid Fuzzy ARTMAP and Online Extreme Learning Machine for Pattern Classification

OriginalPaper

Almost Automorphic Solutions of Impulsive Cellular Neural Networks with Piecewise Constant Argument

OriginalPaper

An Efficient and Effective Multiple Empirical Kernel Learning Based on Random Projection

OriginalPaper

Finite-Time Stability and Its Application for Solving Time-Varying Sylvester Equation by Recurrent Neural Network

OriginalPaper

Classification of Skeletal Wireframe Representation of Hand Gesture Using Complex-Valued Neural Network

OriginalPaper

An Efficient Over-sampling Approach Based on Mean Square Error Back-propagation for Dealing with the Multi-class Imbalance Problem

Neuer Inhalt

    Bildnachweise