Lagrange stability of Delayed Switched Inertial Neural Networks

As an extension of single/multiple stability notion, Lagrange stability is considered here. A class of switched inertial neural networks (SINNs) is studied on both continuous-time and discrete-time domain. Two kinds of activation functions are evolved for the network. Through characteristic function approach, matrix measure strategy, and theory of time scales, Lagrange stability of delayed continuous-/discrete-time SINNs with lurie-type and bounded-type activation functions are addressed, respectively. The results also show that the criteria corresponding to discrete-time network approach to those corresponding to continuous-time one as the graininess function tends to zero. Two examples are given to show the effectiveness of the main results.