Nonlinear Measure Approach for the Stability Analysis of Complex-Valued Neural Networks

Based on the nonlinear measure method and the matrix inequality techniques, this paper addresses the global asymptotic stability for the complex-valued neural networks with delay. Furthermore, robust stability of the addressed neural network with norm-bounded parameter uncertainties is also tackled. By constructing an appropriate Lyapunov functional candidate, several sufficient criteria are obtained to ascertain the existence, uniqueness and global stability of the equilibrium point of the addressed complex-valued neural networks, which are easy to be verified and implemented in practice. Finally, one example is given to illustrate the effectiveness of the obtained results.