Neural networking study of worms in a wireless sensor model in the sense of fractal fractional

被引：13
|
作者：
Khan, Aziz [1 ]
Abdeljawad, Thabet [1 ,2 ,3 ,4 ]
Alqudah, Manar A. [5 ]
机构：
[1] Prince Sultan Univ, Dept Math & Sci, POB 66833, Riyadh 11586, Saudi Arabia
[2] China Med Univ, Dept Med Res, Taichung 40402, Taiwan
[3] Kyung Hee Univ, Dept Phys, 26 Kyungheedae Ro, Seoul 02447, South Korea
[4] Sefako Makgatho Hlth Sci Univ, Dept Math & Appl Math, ZA-0204 Garankuwa, Medusa, South Africa
[5] Princess Nourah Bint Abdulrahman Univ, Fac Sci, Dept Math Sci, POB 84428, Riyadh 11671, Saudi Arabia
来源：
AIMS MATHEMATICS | 2023年 / 8卷 / 11期
关键词：
fractal-fractional operator; neural networking; Mittag-Leffler kernel; Ulam-Hyers stability; Banach contraction; numerical analysis; DIFFERENTIAL-EQUATIONS; STABILITY; EXISTENCE;
D O I：
10.3934/math.20231348
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
We are concerned with the analysis of the neural networks of worms in wireless sensor networks (WSN). The concerned process is considered in the form of a mathematical system in the context of fractal fractional differential operators. In addition, the Banach contraction technique is utilized to achieve the existence and unique outcomes of the given model. Further, the stability of the proposed model is analyzed through functional analysis and the Ulam-Hyers (UH) stability technique. In the last, a numerical scheme is established to check the dynamical behavior of the fractional fractal order WSN model.
引用
收藏
页码：26406 / 26424
页数：19
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