Stability Of Memristor-based Neural Networks Research Articles

Exponential Lagrangian stability and stabilization of memristor-based neural networks with unbounded time-varying delays

Exponential Lagrangian stability and stabilization of memristor-based neural networks with unbounded time-varying delays

A Representation of System Solutions for Global Exponential Stabilization of Memristor-Based Neural Networks With Unbounded Time-Varying Delays

This article mainly discusses the global exponential stabilization of a class of memristor-based neural networks (MNNs) with unbounded time-varying delays. In this article, a new approach without constructing any Lyapunov–Krasovskii functional is proposed to derive the global exponential stabilization criterion containing only a few simple inequalities. This approach is based on the representation of the system solution, which can derive simpler criteria than the existing approaches. In addition, this approach does not limit the range of the derivative of the unbounded time-varying delay, which implies that the stabilization criterion obtained is weaker conservative. Finally, two numerical examples are employed to demonstrate the effectiveness of the approach.

Exponential stabilization of memristor-based neural networks with unbounded time-varying delays

Exponential stabilization of memristor-based neural networks with unbounded time-varying delays

Exponential stability analysis of memristor-based recurrent neural networks with time-varying delays

This paper investigates the exponential stability problem about the memristor-based recurrent neural networks. Having more rich dynamic behaviors, neural networks based on the memristor will play a key role in the optimistic computation and associative memory, therefore, stability analysis of memristor-based neural networks are quite important. Based on the knowledge of memristor and recurrent neural network, the model of the memristor-based recurrent neural networks is established; and the stability of memristor-based neural networks with time-varying delays is studied. Several sufficient conditions for the global exponential stability of these neural networks are presented. These results ensure global exponential stability of memristor-based neural networks in the sense of Filippov solutions. In addition to providing criteria for memristor-based neural networks with time-varying delays, these stability conditions can also be used for memristor-based neural networks with constant time delays or without time delays. Furthermore, it is convenient to estimate the exponential convergence rates of this neural network by using the results. An illustrative example is given to show the effectiveness of the obtained results.