https://doi.org/10.1140/epjp/s13360-024-05488-2
Regular Article
Hopf bifurcations in a fractional-order neural network introducing delays into neutral terms
1
College of Undergraduate Education, Jiangxi Modern Polytechnic College, 330095, Nanchang, China
2
School of Mathematics and Statistics, Xinyang Normal University, 464000, Xinyang, China
3
School of Mathematics and Physics, Guangxi Minzu University, 530006, Nanning, China
b huangchengdai@163.com, chdhuang1986@xynu.edu.cn
Received:
25
June
2024
Accepted:
23
July
2024
Published online:
10
August
2024
This paper addresses a bifurcation analysis of a neutral fractional-order neural network (NFONN) with neutral-type delays on the basis of reducing the degree of transcendental terms with respect to the characteristic equation. The delay-dependent bifurcation results are deduced by analyzing the characteristic equation. Furthermore, some worthwhile phenomena are observed: On the one hand, the developed NFONN possesses illustrious stability performance in comparison with the corresponding integer-order NN. On the other hand, the shorter convergence time of the devised NFONN can be captured by selecting appropriate parameters compared with the traditional delayed NN. The developed approach involves the superiorities with direct operation process, low computational complexity and easy implementation in comparison with some approaches available. Lastly, two numerical examples are adopted to underpin the developed theoretical outcomes.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and 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.
