Simulink comparison of varying-parameter convergent-differential neural-network and gradient neural network for solving online linear time-varying equations

A novel kind of recurrent neural network (called varying-parameter convergent-differential neural-network, VP-CDNN) is proposed in this paper for online solving linear time-varying equations. Different from traditional gradient-base neural network (called GNN) with scalar-valued error functions, such new VP-CDNN is designed based on matrix-valued or vector-valued error functions and their coefficients related to convergence is time-varying. In other words, the coefficients are the functions of time t. In addition, this kind of VP-CDNN is depicted in implicit dynamics but not explicit dynamics. To illustrate the effectiveness of the new neural network, the comparative simulations with MATLAB Simulink of the proposed VP-CDNN and GNN for solving online linear time-varying equations are implemented and presented. Computer-simulation results verify the fast convergence and good robustness.