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Lagrange Stability for Memristor-Based Neural Networks with Time-Varying Delay via Matrix Measure

  • Sanbo Ding
  • Linlin Zhao
  • Zhanshan Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9377)

Abstract

In this paper, we study the global exponential stability in Lagrange sense for memristor-based neural networks (MBNNs) with time-varying delays. Based on the nonsmooth analysis and differential inclusion theory, matrix measure technique is employed to establish some succinct criteria which ensure the Lagrange stability of the considered memristive model. In addition, the new proposed criteria are very easy to verify, and they also enrich and improve the earlier publications. Finally, two example are given to demonstrate the validity of the results.

Keywords

Memristor-based Neural Networks Lagrange Stability Matrix Measure 

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© Springer International Publishing Switzerland 2015

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Authors and Affiliations

  1. 1.School of Information Science and EngineeringNortheastern UniversityShenyangChina
  2. 2.Liaoning Province Product Quality Supervision ProcuratorateShenyangChina

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