Passivity and passification of memristor-based complex-valued recurrent neural networks with interval time-varying delays

In this paper, we made an effort to investigate the passivity and passification of memristor-based complex-valued recurrent neural networks (MCVNNs) with interval time-varying delays. By constructing proper Lyapunov–Krasovskii functional and using the characteristic function method, passivity conditions are derived in terms of linear matrix inequalities (LMIs). Then, based on the derived passivity condition, the desired feedback controller is designed, which ensures the MCVNNs with interval time-varying delays to be passive. Finally, numerical examples are given to illustrate the effectiveness of the proposed theoretical results.