Stability and dissipativity analysis for neutral type stochastic Markovian jump static neural networks with time delays

• Autorzy: Cao Yang , Samidurai R. , Sriraman R.

Identyfikatory

DOI

10.2478/jaiscr-2019-0003

• Języki publikacji: EN

Abstrakty

EN

This paper studies the global asymptotic stability and dissipativity problem for a class of neutral type stochastic Markovian Jump Static Neural Networks (NTSMJSNNs) with time-varying delays. By constructing an appropriate Lyapunov-Krasovskii Functional (LKF) with some augmented delay-dependent terms and by using integral inequalities to bound the derivative of the integral terms, some new sufficient conditions have been obtained, which ensure that the global asymptotic stability in the mean square. The results obtained in this paper are expressed in terms of Strict Linear Matrix Inequalities (LMIs), whose feasible solutions can be verified by effective MATLAB LMI control toolbox. Finally, examples and simulations are given to show the validity and advantages of the proposed results.

Słowa kluczowe

EN

static neural networks; dissipativity analysis; Markovian jump; time-varying delays

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2019
• Tom: Vol. 9, No. 3
• Strony: 189--204
• Opis fizyczny: Bibliogr. 48 poz., rys.

Twórcy

autor

Cao Yang

• Department of Mechanical Engineering, The University of Hong Kong, Pokfulam, Hong Kong

autor

Samidurai R.

• Department of Mathematics, Thiruvalluvar University, Vellore 631 115, India

autor

Sriraman R.

• Department of Mathematics, Thiruvalluvar University, Vellore 631 115, India

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).