State estimation of fractional-order neural networks with time delay
Haibo Bao, Jinde Cao · 2017
This paper investigates the state estimation of fractional-order neural networks (FNNs) with time delay. This is the first to study the state estimation for delayed fractional-order nonlinear system. According to fractional-order Lyapunov direct approach together with linear matrix inequalities (LMIs), sufficient criteria are given to ensure the asymptotical stability of the estimation error system. At last, numerical simulations are exploited to show the validity of the obtained resutls.