Global Robust Stability of a Class of Discrete-Time Interval Neural Networks.

This paper is concerned with global robust stability of a general class of discrete-time interval neural networks which contain time-invariant uncertain parameters with their values being unknown but bounded in given compact sets. We first introduce the concept of diagonally constrained interval neural networks and present a necessary and sufficient condition for global robust stability of the interval networks regardless of the bounds of nondiagonal uncertain parameters of state feedback and connection weight matrices. Then we extend the result to general interval neural networks. Finally, simulation results illustrate the characteristics of the main results. [ABSTRACT FROM AUTHOR]