https://doi.org/10.1140/epjs/s11734-025-01991-4
Regular Article
Chaos dynamics analysis and circuit implementation of a new 4D memristive cellular neural network
College of Physics and Electronic Engineering, Northwest Normal University, 730070, Lanzhou, China
a
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Received:
27
July
2025
Accepted:
23
September
2025
Published online:
6
October
2025
Abstract
To address the limitations of traditional cellular neural network (CNN) in nonlinear dynamic characteristics, this paper first proposes a new flux-controlled memristor model and uses it to replace the resistive elements in the unit output, thus constructing a new 4D memristive cellular neural network (MCNN). Secondly, using stability analysis of equilibrium points, it has been demonstrated that MCNN, under the constraint of a global zero equilibrium point, can trigger dynamic behaviors such as bistable coexistence, multi-period oscillation, and high complexity chaotic states by adjusting the symmetric saddle focus equilibrium point through parameter control. Furthermore, what is even more interesting is that the constructed MCNN exhibits its unique antimonotonicity, which provides new possibilities for neural morphological engineering through mutations or flips. Finally, the effectiveness and feasibility of MCNN are jointly verified through analog circuit simulation and field programmable gate array (FPGA) digital circuit implementation.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
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.
