Unlike feedforward neural networks (FFNN) which can act as universal function approximators, recursive, or recurrent, neural networks can act as universal approximators for multi-valued functions. In this paper, a real time recursive backpropagation (RTRBP) algorithm in a vector matrix form is developed for a two-layer globally recursive neural network that has multiple delays in its feedback path. This algorithm has been evaluated on two GRNNs that approximate both an analytic and nonanalytic periodic multi-valued function that a feedforward neural network is not capable of approximating.
Back propagation (Artificial intelligence); Globally recurrent neural network (GRNN); Hysteresis; Multi-valued periodic functions; Neural networks (Computer science); Periodic functions; Real time recursive backpropagation (RTRBP); Recurrent neural network (RNN); System approximation
A vector matrix real time backpropagation algorithm for recurrent neural networks that approximate multi-valued periodic functions.
International Journal on Computational Intelligence and Application, 8(4),
World Scientific Publishing.