New global exponential stability results for a memristive neural system with time-varying delays
Recent research in memristorCMOS neuromorphic learning systems has led to the practical realization of neuro-inspired learning architectures. At present, the deep understanding of nonlinear dynamical mechanisms governing memristive neural systems is still an open issue. In this paper, the global exponential stability problem is investigated for a class of memristive neural systems with time-varying delays. By employing comparison principle, some novel global exponential stability results are derived. These stability conditions also improve upon some existing results. In addition, the obtained results are convenient to estimate the exponential convergence rate. These theoretical studies are very useful in analyzing the composite behavior of complex memristor circuits.
global exponential stability neuromorphic learning systems comparison principle hybrid systems