Asymptotic Behavior of Delayed Reaction-Diffusion Neural Networks Modeled by Generalized Proportional Caputo Fractional Partial Differential Equations

In this paper, a delayed reaction-diffusion neural network model of fractional order and with several constant delays is considered. Generalized proportional Caputo derivatives respect to the time variable are applied, type derivative generalizes known types in literature for such as derivative. Thus, obtained results additionally generalize some models literature. The long term behavior solution when increasing without bound studied sufficient conditions approaching zero obtained. Lyapunov functions defined sum squares their generalized applied comparison result scalar linear differential equation presented. principle then combined establish our main results.