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.