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|Title:||Stability of Markovian jump generalized neural networks with interval time-varying delays|
Syed Ali, M.
|Citation:||IEEE Transactions on Neural Networks and Learning Systems, 2017; 28(8):1840-1850|
|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 http://www.ieee.org/publications_standards/publications/rights/index.html for more information.|
|Appears in Collections:||Electrical and Electronic Engineering publications|
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