nach oben

Neural Computing and Applications

Erschienen in:

01.10.2015 | Original Article

Stability of Markovian jump neural networks with mode-dependent delays and generally incomplete transition probability

verfasst von: Jing Xie, Yonggui Kao

Erschienen in: Neural Computing and Applications | Ausgabe 7/2015

Einloggen

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

search-config

KI-gestützte Suche

Aus

Abstract

This paper deals with the robust exponential stability problem for a class of Markovian jump neural networks with mode-dependent delays and generally incomplete transition probability. The delays vary randomly depending on the mode of the networks. Each transition rate can be completely unknown, or only its estimate value is known. By using a new Lyapunov–Krasovskii functional, a delay-dependent stability criterion is presented in terms of linear matrix inequalities (LMIs). The proposed LMI results extend the earlier publications. Finally, a numerical example is given to show the effectiveness and efficiency of the results.

Vorheriger Artikel Path following of underactuated surface vessels with fin roll reduction based on neural network and hierarchical sliding mode technique

Nächster Artikel Designing precision fuzzy controller for load swing of an overhead crane

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

Dehyadegary L, Seyyedsalehi SA, Nejadgholi I (2011) Nonlinear enhancement of noisy speech, using continuous attractor dynamics formed in recurrent neural networks. Neurocomputing 74(17):2716–2724CrossRef

Ryeu JK, Chung HS (1996) Chaotic recurrent neural networks and their application to speech recognition. Neurocomputing 13(2–4):281–294CrossRef

Galn CO, Prez JRR, Corts SG, Snchez AB (2013) Analysis of the influence of forestry environments on the accuracy of GPS measurements by means of recurrent neural networks. Math Comput Model 57(7–8):2016–2023CrossRef

Murray JC, Erwin HR, Wermter S (2009) Robotic sound-source localisation architecture using cross-correlation and recurrent neural networks. Neural Netw 22(2):173–189CrossRef

Zhang M, Chu Z (2012) Adaptive sliding mode control based on local recurrent neural networks for underwater robot. Ocean Eng 45:56–62CrossRefMATH

Haykin S (1995) Recurrent neural networks for adaptive filtering. Control Dyn Syst 68:89–119CrossRef

Subrahmanya N, Shin YC (2010) Constructive training of recurrent neural networks using hybrid optimization. Neurocomputing 73(13–15):2624–2631CrossRef

Al Seyab RK, Cao Y (2008) Nonlinear system identification for predictive control using continuous time recurrent neural networks and automatic differentiation. J Process Control 18(6):568–581CrossRef

van den Driessche P, Zou X (1998) Global attractivity in delayed Hopfield neural network models. SIAM J Appl Math 58(6):1878–1890MathSciNetCrossRefMATH

10.

Wu A, Wen S, Zeng Z (2012) Synchronization control of a class of memristor-based recurrent neural networks. Inf Sci 183(1):106–116MathSciNetCrossRefMATH

11.

Samli R, Arik S (2009) New results for global stability of a class of neutral-type neural systems with time delays. Appl Math Comput 210(2):564–570MathSciNetCrossRefMATH

12.

Ensari T, Arik S (2010) New results for robust stability of dynamical neural networks with discrete time delays. Expert Syst Appl 37(8):5925–5930CrossRef

13.

Faydasicok O, Arik S (2013) A new robust stability criterion for dynamical neural networks with multiple time delays. Neurocomputing 99(1):290–297CrossRef

14.

Senan S, Arik S, Liu D (2012) New robust stability results for bidirectional associative memory neural networks with multiple time delays. Appl Math Comput 218(23):11472–11482MathSciNetCrossRefMATH

15.

Arik S (2005) Global robust stability analysis of neural networks with discrete time delays. Chaos Solitons Fractals 26(5):1407–1414MathSciNetCrossRefMATH

16.

Wang Z, Shu H, Fang J, Liu X (2006) Robust stability for stochastic Hopfield neural networks with time delays. Nonlinear Anal Real World Appl 7:1119–1128MathSciNetCrossRefMATH

17.

Wang Z, Fang J, Liu X (2008) Global stability of stochastic high-order neural networks with discrete and distributed delays. Chaos Solitons Fractals 36:388–396MathSciNetCrossRefMATH

18.

Kao Y, Wang C (2013) Global stability analysis for stochastic coupled reaction-diffusion systems on networks. Nonlinear Anal B Real World Appl 14(3):1457–1465MathSciNetCrossRefMATH

19.

Wang C, Kao Y, Yang G (2012) Exponential stability of impulsive stochastic fuzzy reaction-diffusion Cohen–Grossberg neural networks with mixed delays. Neurocomputing 89:55–63CrossRef

20.

Kao Y, Guo J, Wang C, Sun X (2012) Delay-dependent robust exponential stability of Markovian jumping reaction-diffusion Cohen–Grossberg neural networks with mixed delays. J Franklin Inst 349(6):1972–1988MathSciNetCrossRef

21.

Kao Y, Wang C, Zhang L (2013) Delay-dependent exponential stability of impulsive Markovian jumping Cohen–Grossberg neural networks with reaction-diffusion and mixed delays. Neural Process Lett 38(3):321–346CrossRef

22.

Han W, Liu Y, Wang L (2010) Robust exponential stability of Markovian jumping neural networks with mode-dependent delay. Commun Nonlinear Sci Numer Simul 15(9):2529–2535MathSciNetCrossRefMATH

23.

Liu Y (2009) Stochastic asymptotic stability of Markovian jumping neural networks with Markov mode estimation and mode-dependent delays. Phys Lett A 373(41):3741–3742MathSciNetCrossRefMATH

24.

Sun M, Lam J, Xu S, Zou Y (2007) Robust exponential stabilization for Markovian jump systems with mode-dependent input delay. Automatica 43:1799–1807MathSciNetCrossRefMATH

25.

Xu S, Chen T, Lam J (2003) Robust \(H_\infty \) filtering for uncertain Markovian jump systems with mode-dependent time delays. IEEE Trans Autom Control 48(5):900–906MathSciNetCrossRef

26.

Wang Z, Liu Y, Yu L, Liu X (2006) Exponential stability of delayed recurrent neural networks with Markovian jumping parameters. Phys Lett A 356:346–352CrossRefMATH

27.

Kao Y, Xie J, Wang C (2014) Stabilisation of singular Markovian jump systems with generally uncertain transition rates. IEEE Trans Autom Control 59(9):2604–2610MathSciNetCrossRef

28.

Boyd S, Ghaoui LE, Feron E, Balakrishnan V (1994) Linear matrix inequalities in system and control theory. SIAM, PhiladelphiaCrossRefMATH

29.

Gu K (2000) An integral inequality in the stability problem of time-delay systems. 39th IEEE conference on decision and control, Sydney, Australia, vol 11, pp 2805–2810

Titel: Stability of Markovian jump neural networks with mode-dependent delays and generally incomplete transition probability
verfasst von: Jing Xie
Yonggui Kao
Publikationsdatum: 01.10.2015
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1812-9

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Weitere Artikel der Ausgabe 7/2015

Multimedia event detection with ℓ2-regularized logistic Gaussian mixture regression

Use of the image and depth sensors of the Microsoft Kinect for the detection of gait disorders

Modeling of catalyst composition–activity relationship of supported catalysts in NH3–NO-SCR process using artificial neural network

Crossover-based artificial bee colony algorithm for constrained optimization problems

A new security approach for public transport application against tag cloning with neural network-based pattern recognition

Importance of hybrid models for forecasting of hydrological variable