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A Fractional-Order Memristive Two-Neuron-Based Hopfield Neuron Network: Dynamical Analysis and Application for Image Encryption

Author

Listed:
  • Jayaraman Venkatesh

    (Center for Artificial Intelligence, Chennai Institute of Technology, Chennai 600069, Tamil Nadu, India)

  • Alexander N. Pchelintsev

    (Department of Higher Mathematics, Tambov State Technical University, Sovetskaya Str. 106, 392000 Tambov, Russia)

  • Anitha Karthikeyan

    (Department of Electronics and Communication Engineering, Vemu Institute of Technology, Chithoor 517112, Andhra Pradesh, India
    Department of Electronics and Communications Engineering and University Centre for Research & Development, Chandigarh University, Mohali 140413, Punjab, India)

  • Fatemeh Parastesh

    (Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai 600069, Tamil Nadu, India)

  • Sajad Jafari

    (Health Technology Research Institute, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran
    Department of Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran 15916-34311, Iran)

Abstract

This paper presents a study on a memristive two-neuron-based Hopfield neural network with fractional-order derivatives. The equilibrium points of the system are identified, and their stability is analyzed. Bifurcation diagrams are obtained by varying the magnetic induction strength and the fractional-order derivative, revealing significant changes in the system dynamics. It is observed that lower fractional orders result in an extended bistability region. Also, chaos is only observed for larger magnetic strengths and fractional orders. Additionally, the application of the fractional-order model for image encryption is explored. The results demonstrate that the encryption based on the fractional model is efficient with high key sensitivity. It leads to an encrypted image with high entropy, neglectable correlation coefficient, and uniform distribution. Furthermore, the encryption system shows resistance to differential attacks, cropping attacks, and noise pollution. The Peak Signal-to-Noise Ratio (PSNR) calculations indicate that using a fractional derivative yields a higher PSNR compared to an integer derivative.

Suggested Citation

  • Jayaraman Venkatesh & Alexander N. Pchelintsev & Anitha Karthikeyan & Fatemeh Parastesh & Sajad Jafari, 2023. "A Fractional-Order Memristive Two-Neuron-Based Hopfield Neuron Network: Dynamical Analysis and Application for Image Encryption," Mathematics, MDPI, vol. 11(21), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:21:p:4470-:d:1269387
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    References listed on IDEAS

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    4. Zuñiga Aguilar, C.J. & Gómez-Aguilar, J.F. & Alvarado-Martínez, V.M. & Romero-Ugalde, H.M., 2020. "Fractional order neural networks for system identification," Chaos, Solitons & Fractals, Elsevier, vol. 130(C).
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