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Dynamical analysis of neural networks with time-varying delays using the LMI approach
conference contributionposted on 2015-01-01, 00:00 authored by Lakshmanan Shanmugam, Chee Peng LimChee Peng Lim, Asim BhattiAsim Bhatti, D Gao, Saeid NahavandiSaeid Nahavandi
This study is concerned with the delay-range-dependent stability analysis for neural networks with time-varying delay and Markovian jumping parameters. The time-varying delay is assumed to lie in an interval of lower and upper bounds. The Markovian jumping parameters are introduced in delayed neural networks, which are modeled in a continuous-time along with finite-state Markov chain. Moreover, the sufficient condition is derived in terms of linear matrix inequalities based on appropriate Lyapunov-Krasovskii functionals and stochastic stability theory, which guarantees the globally asymptotic stable condition in the mean square. Finally, a numerical example is provided to validate the effectiveness of the proposed conditions.