Novel Asymptotic Synchronization Criteria on Riemann-Liouville Derivative Type Nonidentical Delayed Neural Networks

This article focuses on investigating the asymptotic synchronization of two nonidentical Riemann-Liouville (R-L) fractional-order delayed neural networks (FDNNs). Compared with the existing literature, the considered model is concerned with the nonidentical FDNNs rather than identical NNs. Under a novel feedback controller, two asymptotic synchronization criteria on FDNNs are established by utilizing the Lyapunov direct method. The derived criteria are concise and easy to test in actual applications. A numerical example is given to verify the availability of the presented results for nonidentical FDNNs.