Global Exponential Stability of Reaction-Diffusion Delayed BAM Neural Networks with Dirichlet Boundary Conditions

In this paper, the global exponential stability for a class of reaction-diffusion delayed bidirectional associate memory (BAM) neural networks with Dirichlet boundary conditions is addressed by using the method of variation parameter and inequality technique, the delay-independent sufficient conditions to guarantee the uniqueness and global exponential stability of the equilibrium point of such networks are established. Finally, an example is given to show the effectiveness of the obtained result.

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