Preserving Global Exponential Stability of Hybrid BAM Neural Networks with Reaction Diffusion Terms in the Presence of Stochastic Noise and Connection Weight Matrices Uncertainty

摘要

We study the impact of stochastic noise and connection weight matrices uncertainty on global exponential stability of hybrid BAM neural networks withreaction diffusion terms. Given globally exponentially stable hybrid BAM neuralnetworks with reaction diffusion terms, the question to be addressed here is how muchstochastic noise and connection weights matrices uncertainty the neural networks cantolerate while maintaining global exponential stability. The upper threshold of stochastic noise and connection weights matrices uncertainty is defined by using thetranscendental equations. We find that the perturbed hybrid BAM neural networkswith reaction diffusion terms preserve global exponential stability if the intensity ofboth stochastic noise and connection weights matrices uncertainty is smaller than thedefined upper threshold. A numerical example is also provided to illustrate the theoretical conclusion.