Global exponential stability of memristor based uncertain neural networks with time-varying delays via Lagrange sense

ABSTRACT

This paper addresses the global exponential stability in Lagrange sense for memristor-based neural networks (MNNs) with time-varying delays. This paper attempts to derive the delay-dependent Lagrange stability conditions in terms of linear matrix inequalities by designing a suitable Lyapunov-Krasovskii functionaland used Wirtinger inequality, Jensen-based inequality for estimating the integral inequalities. The conditions which are derived confirms the globally exponential stability in Lagrange sense for the proposed MNNs and, the detailed estimation for global exponential attractive set is also given. To show the effectiveness and applicability of the proposed criteria, two numerical examples are also provided in this paper.

KEYWORDS:

• Memristor neural networks
• lagrange stability
• wirtinger inequality
• jensen-based inequality
• Lyapunov-Krasovskii functional
• linear matrix inequality

Acknowledgements

The work of author was supported by CSIR . 25(0274)/17/EMR-II dated 27/04/2017.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Council of sceintific and Industrial Research CSIR . 25(0274)/17/EMR-II dated 27/04/2017.