Frontiers in Physics (Mar 2022)

A 6D Fractional-Order Memristive Hopfield Neural Network and its Application in Image Encryption

  • Fei Yu,
  • Xinxin Kong,
  • Huifeng Chen,
  • Qiulin Yu,
  • Shuo Cai,
  • Yuanyuan Huang,
  • Sichun Du

DOI
https://doi.org/10.3389/fphy.2022.847385
Journal volume & issue
Vol. 10

Abstract

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This paper proposes a new memristor model and uses pinched hysteresis loops (PHL) to prove the memristor characteristics of the model. Then, a new 6D fractional-order memristive Hopfield neural network (6D-FMHNN) is presented by using this memristor to simulate the induced current, and the bifurcation characteristics and coexistence attractor characteristics of fractional memristor Hopfield neural network is studied. Because this 6D-FMHNN has chaotic characteristics, we also use this 6D-FMHNN to generate a random number and apply it to the field of image encryption. We make a series of analysis on the randomness of random numbers and the security of image encryption, and prove that the encryption algorithm using this 6D-FMHNN is safe and sensitive to the key.

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