Finite-time stabilization of fractional-order fuzzy quaternion-valued BAM neural networks via direct quaternion approach |
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| Institution: | 1. College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China;2. School of Mathematics, Southeast University, Nanjing 210096, China;3. Department of Mathematics, Harbin Institute of Technology at Weihai, Shandong 264209, China;1. Department of Mathematics, Harbin Institute of Technology at Weihai, Shangdong 264209, PR China;2. Department of Mathematics, University of Texas at San Antonio, San Antonio, TX 78249, USA;3. Department of Control Science and Engineering, Harbin Institute of Technology at Weihai, Shangdong 264209, PR China;1. Faculty of Sciences, Beijing University of Technology, Beijing 100124, China;2. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;3. School of Computer Science, Beijing Information Science and Technology University, Beijing 100101, China;1. Department of Mathematics, Harbin Institute of Technology (Weihai), Weihai 264209, China;2. College of Mathematics and System Sciences, Xinjiang University, Urumqi 830046, China |
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| 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. |
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