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

Aziz Khan, Thabet Abdeljawad*, Manar A. Alqudah

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)26406-26424
Number of pages19
JournalAIMS Mathematics
Volume8
Issue number11
DOIs
Publication statusPublished - 2023
Externally publishedYes

Keywords

  • Banach contraction
  • Mittag-Leffler kernel
  • Ulam-Hyers stability
  • fractal-fractional operator
  • neural networking
  • numerical analysis

Fingerprint

Dive into the research topics of 'Neural networking study of worms in a wireless sensor model in the sense of fractal fractional'. Together they form a unique fingerprint.

Cite this