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Get Free AccessThis 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.
Ramasamy Saravanakumar, M. Syed Ali, Choon Ki Ahn, Hamid Reza Karimi, Peng Shi (2016). Stability of Markovian Jump Generalized Neural Networks With Interval Time-Varying Delays. IEEE Transactions on Neural Networks and Learning Systems, 28(8), pp. 1840-1850, DOI: 10.1109/tnnls.2016.2552491.
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Type
Article
Year
2016
Authors
5
Datasets
0
Total Files
0
Language
English
Journal
IEEE Transactions on Neural Networks and Learning Systems
DOI
10.1109/tnnls.2016.2552491
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