Synchronization of fractional order time delayed neural networks using matrix measure approach

This research delves into the utilization of the matrix measure approach (MMA) for the synchronization of fractional order neural networks (FONNs) incorporating time delays. This study introduces a set of criteria for achieving control input within the slave system (FONNs), employing a novel approach based on fractional order Dini-like derivatives within the matrix measure framework. The proposed criteria formulated in fractional order, encompass diverse conditions that align with several sufficient conditions inherent in MMA. Synchronization between the slave and master system is established, ensuring the asymptotic stability between them. Finally, by presenting the numerical result, the efficacy of FONNs synchronization is obtained.