Robust stability of hopfield delayed neural networks via an augmented L-K functional

This paper focuses on the issue of robust stability of artificial delayed neural networks. A free-matrix-based inequality strategy is produced by presenting an arrangement of slack variables, which can be optimized by means of existing convex optimization algorithms. To reflect a large portion of the dynamical behaviors of the framework, uncertain parameters are considered. By constructing an augmented Lyapunov functional, sufficient conditions are derived to guarantee that the considered neural systems are completely stable. The conditions are presented in the form of as linear matrix inequalities (LMIs). Finally, numerical cases are given to show the suitability of the results presented.