LMI conditions for delay probability distribution dependent robust stability analysis of markovian jump stochastic neural networks with time-varying delays

N. Mala; A.R. Sudamani Ramaswamy; A. Vinodkumar

doi:10.26637/MJM0702/0031

Abstract

This paper investigates the robust stability analysis for a class of uncertain stochastic neural networks (SNNs)with markovian jump and time-varying delays. 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

Mathematics Subject Classification:

Mathematics

Authors Imprint References Funding Related Articles Downloads

N. Mala Department of Mathematics, Kovai Kalaimagal College of Arts and Science, Coimbatore-641 109, Tamil Nadu, India.
A.R. Sudamani Ramaswamy Department of Mathematics, Avinashilingam Deemed University for Women, Coimbatore-641 043, Tamil Nadu, India.
A. Vinodkumar Department of Mathematics, Amrita Vishwa Vidyapeetham University, Coimbatore-641 112, Tamil Nadu, India. https://orcid.org/0000-0002-5314-8768

Pages: 353-365
Date Published: 01-04-2019
Vol. 7 No. 02 (2019): Malaya Journal of Matematik (MJM)

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