Influence of multiple time delays on bifurcation of fractional-order neural networks

Changjin Xu, Maoxin Liao, Peiluan Li, Ying Guo, Qimei Xiao and Shuai Yuan

Applied Mathematics and Computation, 2019, vol. 361, issue C, 565-582

Abstract: In this article, on the basis of predecessors, works, we will propose a new fractional-order neural network model with multiple delays. Letting two different delays be bifurcation parameters and analyzing the corresponding characteristic equations of considered model, we will establish a set of new sufficient criteria to guarantee the stability and the appearance of Hopf bifurcation of fractional-order network model with multiple delays. The impact of two different delays on the stability behavior and the emergence of Hopf bifurcation of involved network model is revealed. The influence of the fractional order on the stability and Hopf bifurcation of involved model is also displayed. To check the correctness of analytical results, we perform programmer simulations with software. A conclusion is drawn in the end. The analysis results in this article are innovative and have important theoretical significance in designing neural networks.

Keywords: Neural networks; Hopf bifurcation; Stability; Fractional order; Delay (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:361:y:2019:i:c:p:565-582

DOI: 10.1016/j.amc.2019.05.057

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