Bifurcations of a Fractional-Order Four-Neuron Recurrent Neural Network with Multiple Delays. (29th September 2022)

• Record Type: Journal Article
• Authors: Fei, Yu; Li, Rongli; Meng, Xiaofang; Li, Zhouhong
• Other Names: Liu Heng Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2022(2022)
• Publication Date: 2022-09-29
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2022/1779582
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : This paper investigates the bifurcation issue of fractional-order four-neuron recurrent neural network with multiple delays. First, the stability and Hopf bifurcation of the system are studied by analyzing the associated characteristic equations. It is shown that the dynamics of delayed fractional-order neural networks not only depend heavily on the communication delay but also significantly affects the applications with different delays. Second, we numerically demonstrate the effect of the order on the Hopf bifurcation. Two numerical examples illustrate the validity of the theoretical results at the end.