Input-to-State Stability for Dynamical Neural Networks with Time-Varying Delays

الملخص EN

A class of dynamical neural network models with time-varying delays is considered.

By employing the Lyapunov-Krasovskii functional method and linear matrix inequalities (LMIs) technique, some new sufficient conditions ensuring the input-to-state stability (ISS) property of the nonlinear network systems are obtained.

Finally, numerical examples are provided to illustrate the efficiency of the derived results.