New results on robust finite-time boundedness of uncertain switched neural networks with time-varying delays

This paper investigates the finite-time boundedness (FTB) problem for a class of uncertain switched neural networks with time-varying delays. By exploring the mode-dependent properties of each subsystem, all the subsystems could be categorized into stable and unstable ones under the Lyapunov-like function framework. The sufficient conditions and a set of unified switching signals with average dwell time (ADT) are first derived with a known limit to the total activation time ratio between unstable and stable subsystems. Then, the obtained results are extended to a new switching approach with mode-dependent average dwell time (MDADT). Compared with general results, our proposed approach distinguishes the stable and unstable subsystems rather than viewing all subsystems as being unstable, thus getting less conservative switching criteria. Finally, a numerical example is provided to show the validity and the advantages of the finding techniques.