A Novel Gradient Neural Network for Tackling the Complex-valued System of Linear Equations Online

To increase the convergence speed for tackling the complex-valued system of linear equation (CVSLE) problem online, a novel fully complex-valued gradient neural network (FCGNN) model is proposed. Firstly, to tackle the CVSLE problem in the complex number field instead of the real number field, the negative gradient descent information and a new nonlinear activation function are used to design a FCGNN model. Secondly, the proof for the global convergence property of the FCGNN model is deduced from the strict derivation. Finally, the simulation results show that this FCGNN model has a higher convergence rate than the original gradient neural network (GNN) model and the improved Zhang neural network (IZNN) model.