Abstract
In this paper, the issue of a global asymptotic stability analysis is developed for piecewise homogeneous Markovian jump BAM neural networks with mixed time delays. By establishing the Lyapunov functional, using mode-dependent discrete delay and applying the linear matrix inequality (LMI) method, a novel sufficient condition is obtained to guarantee the stability of the considered system. A numerical example is provided to demonstrate the feasibility and effectiveness of the proposed results.
Highlights
1 Introduction As is well known, the bidirectional associative memory (BAM) neural networks were originally introduced by Kosko [ – ], and they are a class of two-layer heteroassociative networks, which are composed of neurons arranged in two layers, the U-layer and the V-layer
To the best of our knowledge, no results have been given for piecewise homogeneous Markovian jump BAM neural networks with discrete and distributed time delays
By employing the Lyapunov method, using mode-dependent discrete delay and some inequality techniques, sufficient conditions are derived for the global asymptotic stability in the mean square of the piecewise homogeneous Markovian jump BAM neural networks with discrete and distributed time delays
Summary
The bidirectional associative memory (BAM) neural networks were originally introduced by Kosko [ – ], and they are a class of two-layer heteroassociative networks, which are composed of neurons arranged in two layers, the U-layer and the V-layer. The stability analysis problem has been investigated in [ ] for stochastic high-order Markovian jumping neural networks with mixed time delays. The stochastic stability analysis has been investigated for piecewise homogeneous Markovian jump neural networks with mixed time delays in [ ].
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