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Mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks
S Muralisankar1, A Manivannan1, P Balasubramaniam2
1School of Mathematics, Madurai Kamaraj University, Madurai 625 021, Tamil Nadu, India.
Abstract:
The aim of this manuscript is to investigate the mean square delay dependent-probability-distribution stability analysis of neutral type stochastic neural networks with time-delays. The time-delays are assumed to be interval time-varying and randomly occurring. Based on the new Lyapunov-Krasovskii functional and stochastic analysis approach, a novel sufficient condition is obtained in the form of linear matrix inequality such that the delayed stochastic neural networks are globally robustly asymptotically stable in the mean-square sense for all admissible uncertainties. Finally, the derived theoretical results are validated through numerical examples in which maximum allowable upper bounds are calculated for different lower bounds of time-delay.
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