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Type: Journal article
Title: Stability of Markovian jump generalized neural networks with interval time-varying delays
Author: Saravanakumar, R.
Syed Ali, M.
Ahn, C.
Karimi, H.
Shi, P.
Citation: IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(8):1840-1850
Publisher: IEEE
Issue Date: 2017
ISSN: 2162-237X
Statement of
Ramasamy Saravanakumar, Muhammed Syed Ali, Choon Ki Ahn, Hamid Reza Karimi and Peng Shi
Abstract: This paper examines the problem of asymptotic stability for Markovian jump generalized neural networks with interval time-varying delays. Markovian jump parameters are modeled as a continuous-time and finite-state Markov chain. By constructing a suitable Lyapunov–Krasovskii functional (LKF) and using the linear matrix inequality (LMI) formulation, new delay-dependent stability conditions are established to ascertain the mean-square asymptotic stability result of the equilibrium point. The reciprocally convex combination technique, Jensen’s inequality, and the Wirtinger-based double integral inequality are used to handle single and double integral terms in the time derivative of the LKF. The developed results are represented by the LMI. The effectiveness and advantages of the new design method are explained using five numerical examples.
Keywords: Asymptotic stability; generalized neural networks (GNNs); interval time-varying delay; Markovian jump parameters
Rights: © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
RMID: 0030095857
DOI: 10.1109/TNNLS.2016.2552491
Appears in Collections:Electrical and Electronic Engineering publications

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