Synchronization for memristive chaotic neural networks using Wirtinger-based multiple integral inequality

Original Article


This paper investigates the synchronization problem for a class of memristive chaotic neural networks with time-varying delays. Based on te Wirtinger-based double integral inequality, two novel inequalities are proposed, which are multiple integral forms of the Wirtinger-based integral inequality. Next, by applying the reciprocally convex combination approach for high order case and a free-matrix-based inequality, novel delay-dependent conditions are established to achieve the synchronization for the memristive chaotic neural networks. The results are based on dividing the bounding of activation function into two subintervals with equal length. Finally, a numerical example is provided to demonstrate the effectiveness of the theoretical results.


Memristive neural networks Synchronization Wirtinger-based integral inequality Reciprocally convex combination Free-matrix-based inequality 

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of ScienceDalian Jiaotong UniversityDalianPeople’s Republic of China
  2. 2.School of Information Science and EngineeringNortheastern UniversityShenyangPeople’s Republic of China

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