Finite-Time Stabilization of Memristive Neural Networks with Time Delays

This paper investigates the finite-time stabilization (FTS) problem of memristive neural networks (MNNs) with time-varying delays. First, a novel memristive connection weights model is established on the basis of the circuits of neural networks and the switching characteristics of memristor. Compared with the existing models, the improved model can better reflect the characteristics of the memristor. Then, by framing a novel Lyapunov-Krasovskii functional and designing a delayed-feedback controller, new less conservative sufficient criteria are derived for the FTS of MNNs. Eventually, two examples are provided to demonstrate the effectiveness of the results.

Automated summary

This paper investigates the finite-time stabilization problem of memristive neural networks with time-varying delays by establishing a novel connection weights model and designing a delayed-feedback controller. New less conservative criteria for the FTS of MNNs are derived, and two examples are provided to demonstrate the effectiveness of the results.