@inproceedings{b50e3d48389c4804b5439e2d157564f6,
title = "Chaos in The Fractional Variable Order Discrete-Time Neural Networks",
abstract = "In this paper, a three dimensional discrete time Hopfield neural network with commensurate fractional variable order is presented based on the Caputo like difference operator. The dynamics of the proposed system is investigated by means of chaotic attractors, bifurcation diagram and maximum Lyapunov exponents, It is shown that the discrete time Hopfield neural network has complex behaviour for several fractional variable orders and different system parameter values. Moreover, the approximate entropy and the C0 complexity algorithms of the system are performed to prove the existence of chaos. Finally, the corresponding simulations are carried out on Matlab to illustrate the theoretical results.",
keywords = "Hopfield neural network, Lyapunov exponents, bifurcation, chaos, complexity, fractional variable order",
author = "Karoun, \{Rabia Chaimaa\} and Adel Ouannas and Horani, \{Mohammed Al\} and Toufik Ziar and Batiha, \{Iqbal M.\} and Zohir Dibi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023 ; Conference date: 14-03-2023 Through 16-03-2023",
year = "2023",
doi = "10.1109/ICFDA58234.2023.10153184",
language = "English",
series = "2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023",
address = "United States",
}