Robust stability of uncertain Markovian jumping stochastic Cohen-Grossberg type bam neural networks with time-varying delays and reaction diffusion terms

Authors:
C. Vidhya;P. Balasubramaniam
Affiliations:
Department of Mathematics, Gandhigram Rural Institute, Deemed University, Gandhigram, Tamil Nadu, India;Department of Mathematics, Gandhigram Rural Institute, Deemed University, Gandhigram, Tamil Nadu, India
Venue:
Neural, Parallel & Scientific Computations
Year:
2011

Citing 5
Cited 0

Stabilization and destabilization of hybrid systems of stochastic differential equations

Automatica (Journal of IFAC)
Robust stability of stochastic delayed additive neural networks with Markovian switching

Neural Networks
On Global Stability of Delayed BAM Stochastic Neural Networks with Markovian Switching

Neural Processing Letters
Stochastic Exponential Stability for Markovian Jumping BAM Neural Networks With Time-Varying Delays

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Stability analysis of bidirectional associative memory networks with time delays

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the robust exponential stability problem is investigated for a class of uncertain Markovian jumping stochastic Cohen-Grossberg type bidirectional associative memory neural networks (CGBAMNN) with time-varying delays and reaction-diffusion terms. By using the Lyapunov stability theory and linear matrix inequality (LMI) technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. Two numerical examples are given to show the effectiveness of the proposed results.