Orador Iheb Albouchi

Universidade de  Carthage

Título
Quasi-Projective Synchronization of Fractional-Order Discrete-Time Neural Networks with Mixed Delays.

Resumo
The study of artificial neural networks has attracted significant attention due to their complex dynamical behaviors and wide applications in secure communication and information processing. Recently, fractional-order models have emerged as a powerful tool to capture long-term memory effects that classical integer-order models cannot represent. In this presentation, we focus on the synchronization problem for a class of uncertain discrete-time fractional-order neural networks subject to multiple delay constraints, specifically leakage, discrete, and distributed delays. The presence of these multiple delays, combined with parametric uncertainties, makes the dynamical behavior highly complex and significantly complicates the synchronization process. To address this challenge, a novel mathematical foundation based on the Caputo fractional difference operator, the Mittag-Leffler function, and the Laplace transform is rigorously established. This new analytical framework specifically tailored for discrete-time fractional systems provides a robust tool for analyzing the system's dynamics. Based on this proposed inequality and advanced analytical techniques, new sufficient conditions are derived to guarantee the quasi-projective synchronization of the networks despite the presence of uncertainties. Furthermore, a nonlinear feedback controller is designed to achieve the desired synchronization, ensuring that the error between the drive and response systems converges to a bounded region. Finally, the theoretical findings are validated through a detailed numerical example.

Participar através da aplicação Teams:

 https://teams.microsoft.com/meet/3211606324653211?p=fmipHrEzelAK9j6kzM

ID da Reunião: 321 160 632 465 321 1

Código de acesso: PQ2HC3HE