Finite-time stabilization of fractional-order fuzzy quaternion-valued BAM neural networks via direct quaternion approach. Issue 15 (October 2021)

• Record Type: Journal Article
• Authors: Chen, Shenglong; Li, Hong-Li; Kao, Yonggui; Zhang, Long; Hu, Cheng
• Is Part Of: Journal of the Franklin Institute. Volume 358:Issue 15(2021)
• Page Start: 7650
• Page End: 7673
• Publication Date: 2021-10
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2021.08.008
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: This paper investigates fractional-order fuzzy quaternion-valued BAM neural networks (FOFQBAMNNs) without decomposition. By virtue of a novel contraction mapping, the existence and uniqueness of the equilibrium point is yielded. Furthermore, according to some basic knowledge on fractional calculus, inequality techniques of fuzzy logic and reduction to absurdity, some criteria are yielded to guarantee finite-time stabilization of FOFQBAMNNs via original quaternion-valued controllers, and the settling times of corresponding finite-time stabilization are derived. Finally, the feasibility of our obtained theoretical results is illustrated by some numerical simulations.