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Neural Processing Letters

, Volume 40, Issue 3, pp 275–288 | Cite as

A Weakly Connected Memristive Neural Network for Associative Memory

  • Xin Wang
  • Chuandong Li
  • Tingwen Huang
  • Shukai Duan
Article

Abstract

In this paper, we propose a star-like weakly connected memristive neural network which is organized in such a way that each cell only interacts with the central cells. By using the describing function method and Malkin’s theorem the phase deviation of this dynamical network is obtained. And then, under the Hebbian learning rule the phase deviation is designed as a desired model for associative memory. Moreover, we take the store and recall of digital images as an example to demonstrate the performance of associative memory. The main contribution of this paper is supply a useful mechanism which the new potential circuit element memristor can be used to realize the associative.

Keywords

Memristor Associative memory Malkin’s Theorem  Neural networks 

Notes

Acknowledgments

This publication was made possible by NPRP grant # NPRP 4-1162-1-181 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. This work was also supported by Natural Science Foundation of China (grant no: 61374078).

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Xin Wang
    • 1
  • Chuandong Li
    • 1
  • Tingwen Huang
    • 2
  • Shukai Duan
    • 3
  1. 1.College of Computer ScienceChongqing UniversityChongqingChina
  2. 2.Texas A&M University at QatarDohaQatar
  3. 3.School of Electronics and Information EngineeringSouthwest UniversityChongqingChina

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