Multiple ψ-Type Stability and Its Robustness for Recurrent Neural Networks With Time-Varying Delays

In this paper, the psi-type stability and robustness of recurrent neural networks are investigated by using the differential inequality. By utilizing psi-type functions combined with the inequality techniques, some sufficient conditions ensuring psi-type stability and robustness are derived for linear neural networks with time-varying delays. Then, by choosing appropriate Lipschitz coefficient in subregion, some algebraic criteria of the multiple psi-type stability and robust boundedness are established for the delayed neural networks with time-varying delays. For special cases, several criteria are also presented by selecting parameters with easy implementation. The derived results cover both psi-type mono-stability and multiple psi-type stability. In addition, these theoretical results contain exponential stability, polynomial stability, and mu-stability, and they also complement and extend some previous results. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed criteria.