New bifurcation results for fractional BAM neural network with leakage delay. (July 2017)

• Record Type: Journal Article
• Authors: Huang, Chengdai; Meng, Yijie; Cao, Jinde; Alsaedi, Ahmed; Alsaadi, Fuad E.
• Is Part Of: Chaos, solitons and fractals. Volume 100(2017)
• Page Start: 31
• Page End: 44
• Publication Date: 2017-07
• Subjects: Leakage delay -- Stability -- Hopf bifurcation -- Fractional order -- BAM neural network; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2017.04.037
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: The bifurcation of fractional BAM network involving leakage delay is elaborately investigated. The relation between the bifurcation point and the order is fully revealed. The stability performance of the proposed networks is markedly undermined by leakage delay. Abstract: Recently, the dynamics of the fractional delayed neural networks has been considerably concerned. It is illustrated that time delay has a remarkable influence on the dynamical behaviors of the fractional neural networks. Nevertheless, the results of the fractional neural network with leakage delay are extremely few. It is the first time that the stability and bifurcation of fractional BAM neural networks with time delay in leakage terms is examined in the current paper. The stability criterion and the conditions of bifurcation are obtained for the proposed systems with or without leakage delay by selecting time delay as the bifurcation parameter. It is amazing that the leakage delay has a destabilizing influence on the stability performance of such system and they cannot be ignored. Moreover, the relation between the bifurcation point and the order is fully discussed by careful calculation. Finally, numerical examples are addressed to verify the feasibility of the obtained theoretical results.