Robustness analysis for connection weight matrix of global exponential stability recurrent neural networks

This paper analyzes the robustness of global exponential stability of recurrent neural networks subject to parameter uncertainty in connection weight matrix. Given a globally exponentially stable recurrent neural network, the problem to be addressed herein is how much parameter uncertainty in the connection weight matrix that the neural network can remain to be globally exponentially stable. We characterize the upper bounds of the parameter uncertainty for the recurrent neural networks to sustain global exponential stability. A numerical example is provided to illustrate the theoretical result.