https://doi.org/10.1140/epjs/s11734-024-01440-8
Regular Article
A novel memristor-coupled discrete neural network with multi-stability and multiple state transitions
School of Information Science and Engineering, Dalian Polytechnic University, 116034, Dalian, China
a
caoyinghong@dlpu.edu.cn
b
moujun@dlpu.edu.cn
Received:
11
May
2024
Accepted:
5
December
2024
Published online:
7
January
2025
In this paper, a discrete memristor-coupled Tabu learning neural network (MCTLNN) with a new memrister is constructed. The stability of the MCTLNN is also discussed, and the dynamical behavior of the MCTLNN with different parameters is analyzed using the bifurcation diagrams, the spectral entropy (SE) complexity algorithms, and Lyapunov exponent spectrum (LEs). It is found that MCTLNN has complex phenomena of multi-stability and hyperchaos, state transitions, and coexisting attractors. In addition, the complex dynamical behavior of state transition at different initial values was also found. Finally, the MCTLNN is realized using digital signal processing (DSP) techniques. Many of the complex dynamical features presented by the MCTLNN provide insights into the complex mechanisms by which the brain works. This research imitates the principle of biological neuron coupling and can be applied to secrecy and encryption systems.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
