https://www.selleckchem.com/pr....oducts/cobimetinib-g
This paper tackles the global polynomial periodicity (GPP) and global polynomial stability (GPS) for proportional delay Cohen-Grossberg neural networks (PDCGNNs). By adopting two transformations, designing opportune Lyapunov functionals (LFs) with tunable parameters and taking inequality skills, several delay-dependent criteria of GPP and GPS are acquired for the PDCGNNs. Here the GPP is also a kind of global asymptotic periodicity (GAP), but it has obvious convergence rate and convergence order, and its convergence