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 1/2019

11.06.2019

Finite-Time Stability for Caputo–Katugampola Fractional-Order Time-Delayed Neural Networks

verfasst von: Assaad Jmal, Abdellatif Ben Makhlouf, A. M. Nagy, Omar Naifar

Erschienen in: Neural Processing Letters | Ausgabe 1/2019

Einloggen

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

search-config
loading …

Abstract

In this paper, an original scheme is presented, in order to study the finite-time stability of the equilibrium point, and to prove its existence and uniqueness, for Caputo–Katugampola fractional-order neural networks, with time delay. The proposed scheme uses a newly introduced fractional derivative concept in the literature, which is the Caputo–Katugampola fractional derivative. The effectiveness of the theoretical results is shown through simulations for two numerical examples.
Vorheriger Artikel New Results on Periodicity of Non-autonomous Inertial Neural Networks Involving Non-reduced Order Method
Nächster Artikel An Ar2p Deep Learning Architecture for the Discovery and the Selection of Features

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.
Price M, Glass J, Chandrakasan AP (2018) A low-power speech recognizer and voice activity detector using deep neural networks. IEEE J Solid-State Circuits 53(1):66–75
2.
3.
Li Y, Tong S (2017) Adaptive neural networks decentralized FTC design for nonstrict-feedback nonlinear interconnected large-scale systems against actuator faults. IEEE Trans Neural Netw Learn Syst 28(11):2541–2554MathSciNet
4.
Rajchakit G (2017) Stability of control neural networks. Int J Res Sci Eng 3(6):22
5.
Zhang XM, Han QL (2014) Global asymptotic stability analysis for delayed neural networks using a matrix-based quadratic convex approach. Neural Netw 54:57–69MATH
6.
Zhu Q, Cao J (2014) Mean-square exponential input-to-state stability of stochastic delayed neural networks. Neurocomputing 131:157–163
7.
Chen X, Song Q (2013) Global stability of complex-valued neural networks with both leakage time delay and discrete time delay on time scales. Neurocomputing 121:254–264
8.
Xu C, Chen L (2018) Effect of leakage delay on the almost periodic solutions of fuzzy cellular neural networks. J Exp Theor Artif Intell 30(6):993–1011
9.
Xu C, Chen L, Li P (2019) Effect of proportional delays and continuously distributed leakage delays on global exponential convergence of CNNS. Asian J Control 21(5):1–8MathSciNet
10.
Xu C (2018) Local and global Hopf bifurcation analysis on simplified bidirectional associative memory neural networks with multiple delays. Math Comput Simul 149:69–90MathSciNet
11.
Xu C, Tang X, Li P (2018) Existence and global stability of almost automorphic solutions for shunting inhibitory cellular neural networks with time-varying delays in leakage terms on time scales. J Appl Anal Comput 8(4):1033–1049MathSciNet
12.
Xu C, Li P (2018) On anti-periodic solutions for neutral shunting inhibitory cellular neural networks with time-varying delays and D operator. Neurocomputing 275:377–382
13.
Xu C, Li P (2018) Global exponential convergence of fuzzy cellular neural networks with leakage delays, distributed delays and proportional delays. Circuits Syst Signal Process 37(1):163–177MathSciNetMATH
14.
Kamenkov G (1953) On stability of motion over a finite interval of time. J Appl Math Mech 17(2):529–540MathSciNet
15.
Bhat SP, Bernstein DS (1998) Continuous finite-time stabilization of the translational and rotational double integrators. IEEE Trans Autom Control 43(5):678–682MathSciNetMATH
16.
Wang H, Zhu Q (2015) Finite-time stabilization of high-order stochastic nonlinear systems in strict-feedback form. Automatica 54:284–291MathSciNetMATH
17.
Mobayen S (2016) Finite-time stabilization of a class of chaotic systems with matched and unmatched uncertainties: an LMI approach. Complexity 21(5):14–19MathSciNet
18.
Lu K, Xia Y (2015) Finite-time attitude stabilization for rigid spacecraft. Int J Robust Nonlinear Control 25(1):32–51MathSciNetMATH
19.
Engheta N (1996) On fractional calculus and fractional multipoles in electromagnetism. IEEE Trans Antennas Propag 44(4):554–566MathSciNetMATH
20.
21.
Jmal A, Naifar O, Ben Makhlouf A, Derbel N, Hammami MA (2018) Observer-based model reference control for linear fractional-order systems. Int J Digit Signal Smart Syst 2(2):136–149MATH
22.
Jmal A, Naifar O, Ben Makhlouf A, Derbel N, Hammami MA (2018) Sensor fault estimation for fractional-order descriptor one-sided Lipschitz systems. Nonlinear Dyn 91(3):1713–1722MATH
23.
24.
Kaslik E, Sivasundaram S (2012) Nonlinear dynamics and chaos in fractional-order neural networks. Neural Netw 32:245–256MATH
25.
Bao HB, Cao JD (2015) Projective synchronization of fractional-order memristor-based neural networks. Neural Netw 63:1–9MATH
26.
27.
Thuan MV, Binh TN, Huong DC (2018) Finite-time guaranteed cost control of caputo fractional-order neural networks. Asian J Control 22(1):1–10
28.
Peng X, Wu H, Song K, Shi J (2017) Global synchronization in finite time for fractional-order neural networks with discontinuous activations and time delays. Neural Netw 94:46–54
29.
30.
Peng X, Wu H (2018) Robust mittag-leffler synchronization for uncertain fractional-order discontinuous neural networks via non-fragile control strategy. Neural Process Lett 48(3):1521–1542
31.
Liu M, Wu H (2018) Stochastic finite-time synchronization for discontinuous semi-Markovian switching neural networks with time delays and noise disturbance. Neurocomputing 310:246–264
32.
Ran-Chao W, Xin-Dong H, Li-Ping C (2013) Finite-time stability of fractional-order neural networks with delay. Commun Theor Phys 60(2):189MathSciNetMATH
33.
34.
Ke Y, Miao C (2015) Stability analysis of fractional-order Cohen–Grossberg neural networks with time delay. Int J Comput Math 92(6):1102–1113MathSciNetMATH
35.
Yang X, Song Q, Liu Y, Zhao Z (2015) Finite-time stability analysis of fractional-order neural networks with delay. Neurocomputing 152:19–26
36.
37.
Katugampola UN (2011) New approach to a generalized fractional integral. Appl Math Comput 218(3):860–865MathSciNetMATH
38.
Katugampola UN (2014) A new approach to generalized fractional derivatives. Bull Math Anal Appl 6(4):1–15MathSciNetMATH
39.
Kilbas AA, Srivastava HH, Trujillo JJ (2006) Theory and applications of fractional differential equations. Elsevier, AmsterdamMATH
40.
Anderson DR, Ulness DJ (2015) Properties of the Katugampola fractional derivative with potential application in quantum mechanics. J Math Phys 56(6):063502MathSciNetMATH
41.
Wu H, Zhang X, Xue S, Wang L, Wang Y (2016) LMI conditions to global Mittag–Leffler stability of fractional-order neural networks with impulses. Neurocomputing 193:148–154
42.
Wang LF, Wu H, Liu DY, Boutat D, Chen YM (2018) Lur’e Postnikov Lyapunov functional technique to global Mittag–Leffler stability of fractional-order neural networks with piecewise constant argument. Neurocomputing 302:23–32
43.
Kuczma M (2009) An introduction to the theory of functional equations and inequalities: Cauchy’s equation and Jensen’s inequality. Springer Science & Business Media, BerlinMATH
44.
Mitrinovic ND (1970) Analytic inequalities. Springer, New YorkMATH
Metadaten
Titel
Finite-Time Stability for Caputo–Katugampola Fractional-Order Time-Delayed Neural Networks
verfasst von
Assaad Jmal
Abdellatif Ben Makhlouf
A. M. Nagy
Omar Naifar
Publikationsdatum
11.06.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10060-6

Weitere Artikel der Ausgabe 1/2019

Neural Processing Letters 1/2019 Zur Ausgabe

OriginalPaper

Improved GNN Models for Constant Matrix Inversion

OriginalPaper

Exponential Synchronization of Stochastic Memristive Neural Networks with Time-Varying Delays

OriginalPaper

Corpus Analysis Using Relaxed Conjugate Gradient Neural Network Training Algorithm

OriginalPaper

Application of Strong Arcs in m-Polar Fuzzy Graphs

OriginalPaper

From Night to Day: GANs Based Low Quality Image Enhancement

OriginalPaper

Weighted Channel-Wise Decomposed Convolutional Neural Networks

Neuer Inhalt

    Bildnachweise