The neural network models with delays for solving absolute value
  equations
Dongmei Yu Gehao Zhang Cairong Chen Deren Han
An inverse-free neural network model with mixed delays is proposed for
solving the absolute value equation (AVE) $Ax -|x| - b =0$, which includes an
inverse-free neural network model with discrete delay as a special case. By
using the Lyapunov-Krasovskii theory and the linear matrix inequality (LMI)
method, the developed neural network models are proved to be exponentially
convergent to the solution of the AVE. Compared with the existing neural
network models for solving the AVE, the proposed models feature the ability of
solving a class of AVE with $\|A^{-1}\|>1$. Numerical simulations are given to
show the effectiveness of the two delayed neural network models.