Global lagrange stability of complex-valued neural networks of neutral type with time-varying delays

In this article, the problem of global exponential stability in Lagrange sense of neutral type complex-valued neural networks (CVNNs) with delays is investigated. Two different classes of activation functions are considered, one can be separated into real part and imaginary part, and the other cannot be separated. Based on Lyapunov theory and analytic techniques, delay-dependent criteria are provided to ascertain the aforementioned CVNNs to be globally exponentially stable GES in Lagrange sense. Moreover, the proposed sufficient conditions are presented in the form of linear matrix inequalities which could be easily checked by Matlab. Finally, two simulation examples are given out to demonstrate the validity of theory results. (c) 2016 Wiley Periodicals, Inc. Complexity 21: 438-450, 2016