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
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
ATZ-Fachkongress

Chassis.tech plus 2024


4 Kongresse in einer Veranstaltung

Treffen Sie in München die Fachleute von Chassis, Lenkung, Bremsen sowie Reifen/Räder.
Erweiterte Suche
Anmelden
nach oben
Neural Computing and Applications
Erschienen in: Neural Computing and Applications 9/2020

24.11.2018 | Original Article

Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach

verfasst von: Wanli Zhang, Shiju Yang, Chuandong Li, Hongfei Li

Erschienen in: Neural Computing and Applications | Ausgabe 9/2020

Einloggen

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

search-config
loading …

Abstract

By using 1-norm-based analytical approach, this paper considers finite-time (FET) synchronization for memristive neural networks (MNNs) with time-varying delays. New quantized controllers are designed, which can save communication channel and play an important role in synchronizing MNNs. By constructing Lyapunov function, and developing 1-norm-based analytical methods, several conditions are derived to guarantee that the MNNs can be synchronized within a settling time. In addition, the settling time is also presented for the considered MNNs. Some numerical simulations are provided to illustrate the theoretical results.
Vorheriger Artikel Semi-parametric training of autoencoders with Gaussian kernel smoothed topology learning neural networks
Nächster Artikel An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata

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.
Watts DJ, Strogatz SH (1998) Collective dynamics of ‘small-world’ networks. Nature 393:440–442MATH
2.
Kwok T, Smith KA (1999) A unified framework for chaotic neural-network approaches to combinatorial optimization. IEEE Trans Neural Netw 10(4):978–981
3.
Huberman BA, Adamic LA (1999) Growth dynamics of the world-wide-web. Nature 401:131–132
4.
Stogatz SH, Stewart I (1993) Coupled oscillators and biological synchronization. Sci Am 269(6):102–109
5.
Hoppensteadt FC, Izhikevich EM (2000) Pattern recognition via synchronization in phase-locked loop neural networks. IEEE Trans Neural Netw 11(3):734–738
6.
Li C, Liao X, Wong K (2005) Lag synchronization of hyperchaos with application to secure communications. Chaos Solitons Fractals 23(1):183–193MathSciNetMATH
7.
Wang Q, Yu S, Li C, Lü J, Fang X, Guyeux C, Bahi JM (2016) Theoretical design and FPGA-based implementation of higher-dimensional digital chaotic systems. IEEE Trans Circuits Syst I Regul Pap 63(3):401–412MathSciNet
8.
Wang J, Wu H, Huang T, Ren S, Wu J (2018) Passivity and output synchronization of complex dynamical networks with fixed and adaptive coupling strength. IEEE Trans Neural Netw Learn Syst 29(2):364–376MathSciNet
9.
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 23:866–875
10.
Li X, Rakkiyappan R (2013) Impulsive controller design for exponential synchronization of chaotic neural networks with mixed delays. Commun Nonlinear Sci Numer Simul 18(6):1515–1523MathSciNetMATH
11.
Vincent UE, Guo R (2011) Finite-time synchronization for a class of chaotic and hyperchaotic systems via adaptive feedback controller. Phys Lett A 375:2322–2326MATH
12.
Aghababa MP, Khanmohammadi S, Alizadeh G (2011) Finite-time synchronization of two different chaotic systems with unknown parameters via sliding mode technique. Appl Math Model 35(6):3080–3091MathSciNetMATH
13.
14.
Bhat S, Bernstein D (1997) Finite-time stability of homogeneous systems. In: Proceedings of American control conference, pp 2513–2514
15.
Shen J, Cao J (2011) Finite-time synchronization of coupled neural networks via discontinuous controllers. Cognit Neurodyn 5(4):373–385
16.
Aghababa MP, Aghababa HP (2012) Synchronization of mechanical horizontal platform systems in finite time. Appl Math Model 36(10):4579–4591MathSciNetMATH
17.
Xu C, Yang X, Lu J, Feng J, Alsaadi FE, Hayat T (2018) Finite-time synchronization of networks via quantized intermittent pinning control. IEEE Trans Cybern 48(10):3021–3027
18.
Huang T, Li C, Yu W, Chen G (2009) Synchronization of delayed chaotic systems with parameter mismatches by using intermittent linear state feedback. Nonlinearity 22:569–584MathSciNetMATH
19.
Guan Z, Liu Z, Feng G, Wang Y (2010) Synchronization of complex dynamical networks with time-varying delays via impulsive distributed control. IEEE Trans Circuits Syst 57(8):2182–2195MathSciNet
20.
Wang L, Xiao F (2010) Finite-time consensus problems for networks of dynamic agents. IEEE Trans Autom Control 55(4):950–955MathSciNetMATH
21.
Yang X, Wu Z, Cao J (2013) Finite-time synchronization of complex networks with nonidentical discontinuous nodes. Nonlinear Dyn 73(4):2313–2327MathSciNetMATH
22.
23.
Forti M, Grazzini M, Nistri P, Pancioni L (2006) Generalized Lyapunov approach for convergence of neural networks with discontinuous or non-Lipschitz activations. Physica D 214(1):88–99MathSciNetMATH
24.
Efimov D, Polyakov A, Fridman E, Perruquetti W, Richard JP (2014) Comments on finite-time stability of time-delay systems. Automatica 50:1944–1947MathSciNetMATH
25.
Yang X (2014) Can neural networks with arbitrary delays be finite-timely synchronized. Neurocomputing 143:275–281
26.
Yang X, Song Q, Liang J, He B (2015) Finite-time synchronization of coupled discontinuous neural networks with mixed delays and nonidentical perturbations. J Frankl Inst 352(10):4382–4406MathSciNetMATH
27.
Yang X, Lu J (2016) Finite-time synchronization of coupled networks with Markovian topology and impulsive effects. IEEE Trans Autom Control 61(8):2256–2261MathSciNetMATH
28.
Zhang W, Yang X, Xu C, Feng J, Li C (2018) Finite-time synchronization of discontinuous neural networks with delays and mismatched parameters. IEEE Trans Neural Netw Learn Syst 29(8):3761–3771MathSciNet
29.
Jia Q, Tang WKS (2018) Event-triggered protocol for the consensus of multi-agent systems with state-dependent nonlinear coupling. IEEE Trans Circuits Syst I Regul Papers 65(2):723–732
30.
31.
Brockett RW, Liberzon D (2000) Quantized feedback stabilization of linear systems. IEEE Trans Autom Control 45:1279–1289MathSciNetMATH
32.
Fu M, Xie L (2005) The sector bound approach to quantized feedback control. IEEE Trans Autom Control 50:1698–1710MathSciNetMATH
33.
Tian E, Yue D, Peng C (2008) Quantized output feedback control for networked control systems. Inf Sci 178(12):2734–2749MathSciNetMATH
34.
Xiao X, Zhou L, Zhang Z (2014) Synchronization of chaotic Lur’e systems with quantized sampled-data controller. Commun Nonlinear Sci Numer Simul 19(6):2039–2047MathSciNetMATH
35.
Wan Y, Cao J, Wen G (2017) Quantized synchronization of chaotic neural networks with scheduled output feedback control. IEEE Trans Neural Netw Learn Syst 28(11):2638–2647MathSciNet
36.
Zhang W, Yang S, Li C, Zhang W, Yang X (2018) Stochastic exponential synchronization of memristive neural networks with time-varying delays via quantized control. Neural Netw 104:93–103
37.
Itoh M, Chua LO (2009) Memristor cellular automata and memristor discrete-time cellular neural networks. Int J Bifurc Chaos 19(11):3605–3656MATH
38.
Thomas A (2013) Memristor-based neural networks. J Phys D 46(9):093001
39.
Wu A, Zeng Z (2012) Dynamic behaviors of memristor-based recurrent neural networks with time-varying delays. Neural Netw 36:1–10MATH
40.
Wen S, Zeng Z, Huang T (2013) Dynamic behaviors of memristor-based delayed recurrent networks. Neural Comput Appl 23:815–821
41.
Yang X, Cao J, Yu W (2014) Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays. Cognit Neurodyn 8(3):239–249
42.
Wang G, Shen Y (2014) Exponential synchronization of coupled memristive neural networks with time delays. Neural Comput Appl 24:1421–1430
43.
Zhang W, Li C, Huang T, He X (2015) Synchronization of memristor-based coupling recurrent neural networks with time-varying delays and impulses. IEEE Trans Neural Netw Learn Syst 26(12):3308–3313MathSciNet
44.
Wang L, Shen Y, Zhang G (2016) Finite-time stabilization and adaptive control of memristor-based delayed neural networks. IEEE Trans Neural Netw Learn Syst 28(11):2648–2659MathSciNet
45.
Yang X, Ho Daniel W C (2016) Synchronization of delayed memristive neural networks: robust analysis approach. IEEE Trans Cybern 46(12):3377–3387
46.
Filippov AF (1988) Differential equations with discontinuous righthand sides. Kluwer, Dordrecht
47.
Aubin J-P, Cellina A (1984) Differential inclusions. Springer, BerlinMATH
Metadaten
Titel
Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach
verfasst von
Wanli Zhang
Shiju Yang
Chuandong Li
Hongfei Li
Publikationsdatum
24.11.2018
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-018-3906-2

Weitere Artikel der Ausgabe 9/2020

Neural Computing and Applications 9/2020 Zur Ausgabe

Original Article

A tree-BLSTM-based recognition system for online handwritten mathematical expressions

Original Article

Salp swarm optimizer to solve optimal power flow comprising voltage stability analysis

Original Article

A scalable indoor localization algorithm based on distance fitting and fingerprint mapping in Wi-Fi environments

Original Article

A CNNs-based method for optical flow estimation with prior constraints and stacked U-Nets

Emerging Trends of Applied Neural Computation - E_TRAINCO

Features extraction from human eye movements via echo state network

Original Article

Edge computing-based real-time passenger counting using a compact convolutional neural network

Premium Partner

    Bildnachweise