TY - GEN
T1 - Finite-Time Stability of ABC Type ℏ-Fractional Discrete Neural Networks
T2 - 2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023
AU - Hioual, Amel
AU - Ouannas, Adel
AU - Momani, Shaher
AU - Oussaeif, Taki Eddine
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The dynamics of fractional-order difference neural networks are currently a major research area, with several noteworthy discoveries. The dynamics of discrete-time neural networks with -fractional nonlocal and nonsingular kernels, on the other hand, have not been thoroughly researched, and this paper is one of the first to address this subject. The main focus of this research is the finite-time stability of discrete-time neural networks based on the nabla ABC fractional difference operator. First, the Atangana-Baleanu -fractional difference sum operator is used to investigate a generalized -Gronwall inequality. This inequality also yields the uniqueness theorem and the finite-time stability criterion of nonlinear -fractional neural networks. Finally, several examples are offered to show the effectiveness of our theoretical conclusion.
AB - The dynamics of fractional-order difference neural networks are currently a major research area, with several noteworthy discoveries. The dynamics of discrete-time neural networks with -fractional nonlocal and nonsingular kernels, on the other hand, have not been thoroughly researched, and this paper is one of the first to address this subject. The main focus of this research is the finite-time stability of discrete-time neural networks based on the nabla ABC fractional difference operator. First, the Atangana-Baleanu -fractional difference sum operator is used to investigate a generalized -Gronwall inequality. This inequality also yields the uniqueness theorem and the finite-time stability criterion of nonlinear -fractional neural networks. Finally, several examples are offered to show the effectiveness of our theoretical conclusion.
KW - ABC ħ-fractional difference
KW - Discrete-time fractional order neural networks
KW - Finite-time stability
KW - Generalized ħ-fractional Gronwall's inequality
KW - Uniqueness
UR - https://www.scopus.com/pages/publications/85164534815
U2 - 10.1109/ICFDA58234.2023.10153373
DO - 10.1109/ICFDA58234.2023.10153373
M3 - Conference contribution
AN - SCOPUS:85164534815
T3 - 2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023
BT - 2023 International Conference on Fractional Differentiation and Its Applications, ICFDA 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 March 2023 through 16 March 2023
ER -