Global asymptotic stability of delayed bi-directional associative memory neural networks
Applied Mathematics and Computation
Delay-dependent stability analysis for impulsive BAM neural networks with time-varying delays
Computers & Mathematics with Applications
Neural Computing and Applications
Impulsive control and synchronization for delayed neural networks with reaction-diffusion terms
IEEE Transactions on Neural Networks
Journal of Computational and Applied Mathematics
IEEE Transactions on Neural Networks
Stability analysis of stochastic reaction-diffusion delayed neural networks with Levy noise
Neural Computing and Applications
IEEE Transactions on Neural Networks
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In this paper, we investigate the exponential stability of stochastic reaction-diffusion Bi-directional Associative Memory (BAM) neural networks. By constructing a novel Lyapunov-Krasovskii function, and applying inequality analysis technique as well as M-matrix theory, we first give some sufficient exponential stability criteria in terms of p-norm for a class of high-order stochastic reaction-diffusion BAM neural networks with discrete and distributed delays. The model we formulated is new and more general than the BAM neural networks investigated in previous publications. Moreover, the obtained results are easy to check and improve some existing stability results. An example is presented to show the application of the criteria obtained in this paper.