Lagrange α-exponential stability and α-exponential convergence for fractional-order complex-valued neural networks

This paper deals with the problem on Lagrange alpha-exponential stability and alpha-exponential convergence for a class of fractional-order complex-valued neural networks. To this end, some new fractional-order differential inequalities are established, which improve and generalize previously known criteria. By using the new inequalities and coupling with the Lyapunov method, some effective criteria are derived to guarantee Lagrange alpha-exponential stability and alpha-exponential convergence of the addressed network. Moreover, the framework of the alpha-exponential convergence ball is also given, where the convergence rate is related to the parameters and the order of differential of the system. These results here, which the existence and uniqueness of the equilibrium points need not to be considered, generalize and improve the earlier publications and can be applied to monostable and multistable fractional-order complex-valued neural networks. Finally, one example with numerical simulations is given to show the effectiveness of the obtained results. (C) 2017 Elsevier Ltd. All rights reserved.