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 1/2022

12.10.2021

Further Results on Input-to-State Stability of Stochastic Cohen–Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays

verfasst von: A. Chandrasekar, T. Radhika, Quanxin Zhu

Erschienen in: Neural Processing Letters | Ausgabe 1/2022

Einloggen

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

search-config
loading …

Abstract

In this article, the problem of stochastic Cohen–Grossberg Bidirectional Associative Memory (CGBAM) neural networks with probabilistic time-varying delay is analyzed by input-to-state stability theory. The stochastic variable with Bernoulli distribution gives the information of probabilistic time-varying delay and it is transformed into one with deterministic time-varying delay in the stochastic manner. Further, by constructing a novel Lyapunov–Krasovskii functional and utilizing Ito’s and Dynkin’s formula with stochastic analysis theory, the sufficient criterion is derived for the input-to-state stability of stochastic CGBAM neural networks. Finally, numerical examples are provided to examine the merits of the given method.
Vorheriger Artikel The Boundedness and the Global Mittag-Leffler Synchronization of Fractional-Order Inertial Cohen–Grossberg Neural Networks with Time Delays
Nächster Artikel Ownership Recommendation via Iterative Adversarial Training

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.
2.
Zhu Q, Huang C, Yang X (2011) Exponential stability for stochastic jumping BAM neural networks with time-varying and distributed delays. Nonlinear Anal Hybrid Syst 5:52–77MathSciNetCrossRef
3.
Zhao Y, Kurthsb J, Duan L (2019) Input-to-state stability analysis for Memristive BAM neural networks with variable time delays. Phys Lett A 383:1143–1150MathSciNetCrossRef
4.
Xu G, Bao H (2020) Further results on mean-square exponential input-to-state stability of time-varying delayed BAM neural networks with Markovian switching. Neurocomputing 376:191–201CrossRef
5.
Guo Y, Luo Y, Wang W, Luo X, Ge C, Kurths J, Yuan M, Gao Y (2019) Fixed-time synchronization of complex-valued memristive BAM neural network and applications in image encryption and decryption. Int J Control Autom Syst 17:1–15CrossRef
6.
Cong Y, Han X, Zhang X (2020) Global exponential stability analysis of discrete-time BAM neural networks with delays: a mathematical induction approach. Neurocomputing 379:227–235CrossRef
7.
Rao R, Zhong S, Wang X (2014) Stochastic stability criteria with LMI conditions for Markovian jumping impulsive BAM neural networks with mode-dependent time-varying delays and nonlinear reaction-diffusion. Commun Nonlinear Sci Numer Simul 19:258–273MathSciNetCrossRef
8.
Zhu Q, Rakkiyappan R, Chandrasekar A (2014) Stochastic stability of Markovian jump BAM neural networks with leakage delays and impulse control. Neurocomputing 136:136–151CrossRef
9.
Du Y, Zhong S, Zhou N (2014) Global asymptotic stability of Markovian jumping stochastic Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. Appl Math Comput 243:624–636MathSciNetMATH
10.
Li X, Fu X (2011) Global asymptotic stability of stochastic Cohen–Grossberg type BAM neural networks with mixed delays: an LMI approach. J Comput Appl Math 235:3385–3394MathSciNetCrossRef
11.
Li K, Zhang L, Zhang X, Li Z (2010) Stability in impulsive Cohen–Grossberg type BAM neural networks with distributed delays. Appl Math Comput 215:3970–3984MathSciNetMATH
12.
Zhang Z, Liu W, Zhou D (2012) Global asymptotic stability to a generalized Cohen–Grossberg BAM neural networks of neutral type delays. Neural Netw 25:94–105CrossRef
13.
Chaouki A, Farah D (2020) New results on interval general Cohen–Grossberg BAM neural networks. J Syst Sci Complex 33:944–967MathSciNetCrossRef
14.
Yang W, Yu W, Cao J, Alsaadi FE, Hayat T (2018) Global exponential stability and lag synchronization for delayed memristive fuzzy Cohen–Grossberg BAM neural networks with impulses. Neural Netw 98:122–153CrossRef
15.
Maharajan C, Raja R, Cao J, Rajchakit G, Alsaedi A (2018) Impulsive Cohen–Grossberg BAM neural networks with mixed time-delays: an exponential stability analysis issue. Neurocomputing 275:2588–2602CrossRef
16.
Li H, Li C, Huang T, Zhang W (2018) Fixed-time stabilization of impulsive Cohen–Grossberg BAM neural networks. Neural Netw 98:203–211CrossRef
17.
18.
Hui M, Zhang J, Zhang J, Iu HC, Yao R, Bai L (2021) Finite-time projective synchronization of stochastic complex-valued neural networks with probabilistic time-varying delays. IEEE Access. 9:44784–44796CrossRef
19.
Liu H, Wang Z, Shen B, Huang T, Alsaadi FE (2018) Stability analysis for discrete-time stochastic memristive neural networks with both leakage and probabilistic delays. Neural Netw 102:1–9CrossRef
20.
Sriraman R, Cao Y, Samidurai R (2020) Global asymptotic stability of stochastic complex-valued neural networks with probabilistic time-varying delays. Math Comput Simul 171:103–118MathSciNetCrossRef
21.
Nagamani G, Radhika T (2015) Dissipativity and passivity analysis of T-S fuzzy neural networks with probabilistic time-varying delays: a quadratic convex combination approach. Nonlinear Dyn 82:1325–1341MathSciNetCrossRef
22.
23.
Angeli D (2002) A Lyapunov approach to incremental stability properties. IEEE Trans Autom Control 47:410–422MathSciNetCrossRef
24.
Arslan G, Basar T (2001) Disturbance attenuating controller design for strict-feedback systems with structurally unknown dynamics. Automatica 37:1175–1188MathSciNetCrossRef
25.
Dashkovskiy S, Ruffer B, Wirth F (2010) Small gain theorems for large scale systems and construction of ISS Lyapunov functions. SIAM J Control Optim 48:4089–4118MathSciNetCrossRef
26.
Freeman RA, Kokotovic PV (2008) Robust nonlinear control design: state-space and Lyapunov techniques. Birkhauser, BostonMATH
27.
Faydasicok O (2020) New criteria for global stability of neutral-type Cohen–Grossberg neural networks with multiple delays. Neural Netw 125:330–337CrossRef
28.
Ali MS, Saravanan S, Esther Rani M, Elakkia S, Cao J, Alsaedi A, Hayat T (2017) Asymptotic stability of Cohen–Grossberg BAM neutral type neural networks with distributed time varying delays. Neural Process Lett 46:991–1007CrossRef
29.
Li Y, Chen X, Zhao L (2009) Stability and existence of periodic solutions to delayed Cohen–Grossberg BAM neural networks with impulses on time scales. Neurocomputing 72:1621–1630CrossRef
30.
Wen Z, Sun J (2009) Stability analysis of delayed Cohen–Grossberg BAM neural networks with impulses via nonsmooth analysis. Chaos Solitons Fractals 42:1829–1837MathSciNetCrossRef
31.
Li X (2009) Exponential stability of Cohen–Grossberg-type BAM neural networks with time-varying delays via impulsive control. Neurocomputing 73:525–530CrossRef
32.
Li X (2009) Existence and global exponential stability of periodic solution for impulsive Cohen–Grossberg-type BAM neural networks with continuously distributed delays. Appl Math Comput 215:292–307MathSciNetMATH
33.
Xiang H, Cao J (2009) Exponential stability of periodic solution to Cohen–Grossberg-type BAM networks with time-varying delays. Neurocomputing 72:1702–1711CrossRef
34.
Du Y, Zhong S, Zhou N, Shi K, Cheng J (2014) Exponential stability for stochastic Cohen–Grossberg BAM neural networks with discrete and distributed time-varying delays. Neurocomputing 127:144–151CrossRef
35.
Rakkiyappan R, Balasubramaniam P (2010) Delay-probability-distribution-dependent stability of uncertain stochastic genetic regulatory networks with mixed time-varying delays: an LMI approach. Nonlinear Anal Hybrid Syst 4:600–607MathSciNetCrossRef
36.
Zhao Y, Kurths J, Duan L (2018) Input-to-state stability analysis for memristive Cohen–Grossberg-type neural networks with variable time delays. Chaos Solitons Fractals 114:364–369MathSciNetCrossRef
37.
Li J, Zhou W, Yang Z (2017) State estimation and input-to-state stability of impulsive stochastic BAM neural networks with mixed delays. Neurocomputing 227:37–45CrossRef
38.
Guo R, Zhang Z, Liu X, Lin C, Wang H, Chen J (2018) Exponential input-to-state stability for complex-valued memristor-based BAM neural networks with multiple time-varying delays. Neurocomputing 275:2041–2054CrossRef
Metadaten
Titel
Further Results on Input-to-State Stability of Stochastic Cohen–Grossberg BAM Neural Networks with Probabilistic Time-Varying Delays
verfasst von
A. Chandrasekar
T. Radhika
Quanxin Zhu
Publikationsdatum
12.10.2021
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2022
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-021-10649-w

Weitere Artikel der Ausgabe 1/2022

Neural Processing Letters 1/2022 Zur Ausgabe

OriginalPaper

Synchronization of Discrete-Time Switched 2-D Systems with Markovian Topology via Fault Quantized Output Control

OriginalPaper

A Novel Fast Fixed-Time Control Strategy and Its Application to Fixed-Time Synchronization Control of Delayed Neural Networks

OriginalPaper

A Maximum Consensus Improvement Method for Group Decision Making Under Social Network with Probabilistic Linguistic Information

OriginalPaper

Pseudo-State Estimation for Fractional Order Neural Networks

OriginalPaper

State Estimation for Genetic Regulatory Networks with Two Delay Components by Using Second-Order Reciprocally Convex Approach

OriginalPaper

Solving Mixed Variational Inequalities Via a Proximal Neurodynamic Network with Applications

Neuer Inhalt

    Bildnachweise