Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
409505 | Neurocomputing | 2015 | 8 Pages |
Abstract
In this paper, a general class of memristive recurrent neural networks with time-varying delays is considered. Based on the knowledge of memristor and recurrent neural networks (RNNs), a model of memristive based RNNs is established. After that the problem of reliable stabilization is studied by constructing a suitable Lyapunov–Krasovskii functional (LKF) and using linear matrix inequality (LMI) framework. By use of the Wirtinger-type inequality, sufficient conditions are presented for the existence of a reliable state feedback controller, which can guarantee the global asymptotic stability of the memristive RNNs. Finally, an example is given to illustrate the theoretical results via numerical simulations.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
K. Mathiyalagan, R. Anbuvithya, R. Sakthivel, Ju H. Park, P. Prakash,