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\((\mu ,\nu )\)-Pseudo-almost automorphic solutions for high-order Hopfield bidirectional associative memory neural networks

  • Chaouki Aouiti
  • Farah Dridi
Original Article
  • 39 Downloads

Abstract

This article is concerned with a high-order Hopfield bidirectional associative memory neural networks with time-varying coefficients and mixed delays. Sufficient conditions are derived for the existence, the uniqueness and the exponential stability of \((\mu ,\nu )\)-pseudo-almost automorphic solutions of the considered model. Banach fixed-point theorem is applied for the existence and the uniqueness results. Global exponential stability is derived via differential inequalities. Finally, two examples are provided to support the feasibility of the theoretical results.

Keywords

\((\mu , \nu )\)-Pseudo-almost automorphic function High-order BAM neural networks Global exponential stability 

Mathematics Subject Classification

34C27 37B25 92C20 

Notes

Compliance with ethical standards

Conflict of interest

There is no conflict of interest.

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Copyright information

© The Natural Computing Applications Forum 2018

Authors and Affiliations

  1. 1.Department of Mathematics, Faculty of Sciences of Bizerta, Research Units of Mathematics and Applications UR13ES47University of CarthageZarzouna, BizertaTunisia

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