LMI conditions for delay probability distribution dependent robust stability analysis of markovian jump stochastic neural networks with time-varying delays
Authors :
N. Mala 1 *, A.R. Sudamani Ramaswamy 2 and A. Vinodkumar 3
Author Address :
1 Department of Mathematics, Kovai Kalaimagal College of Arts and Science, Coimbatore-641 109, Tamil Nadu, India.
2 Department of Mathematics, Avinashilingam Deemed University for Women, Coimbatore-641 043, Tamil Nadu, India.
3 Department of Mathematics, Amrita Vishwa Vidyapeetham University, Coimbatore-641 112, Tamil Nadu, India.
*Corresponding author.
Abstract :
This paper investigates the robust stability analysis for a class of uncertain stochastic neural networks (SNNs)with markovian jump and time-varying delays. %The information of probability distribution of the time delay is considered and transformed into parameter matrices of the transferred stochastic neutral networks model. Based on the stochastic analysis approach & Lyapunov-Krasovskii functional, a delay probability distribution dependent sufficient condition is obtained in the linear matrix inequality (LMI) form such that delayed {markovian jump} SNNs are robustly globally asymptotically stable in the mean square for all admissible uncertainties. An important feature of the result is that the stability conditions are dependent on the probability distribution of delays and upper bound of the derivative is allowed to be greater than or equal to 1.Numerical examples are given for the comparison to illustrate the effectiveness of our results.
Keywords :
Delay probability distribution dependent, Linear matrix inequality, Lyapunov-Krasovskii functional, Markovian jump stochastic neural networks.
DOI :
Article Info :
Received : March 24, 2019; Accepted : May 09, 2019.
