Nonlinearly Activated Complex-Valued Gradient Neural Network for Complex Matrix Inversion

A new nonlinear activated complex-valued gradient neural network is proposed, which is used for complex matrix inversion in the complex area. This work focuses on the inversion of complex-valued matrices in complex domains rather than finding the inverse of real-valued matrices. Compared with the traditional linear complex gradient neural network (GNN), the main contribution of this paper introduces a nonlinear activation function, which can effectively improve the convergence rate of the GNN model. The computer simulation substantiate the effectiveness and superiority of nonlinearly activated complex-valued gradient neural network (NACVGNN) to the matrix inversion, as compared with the linear complex gradient neural network.