Adaptive natural gradient learning algorithms for Mackey–Glass chaotic time prediction

Abstract

In this paper, we introduce the adaptive natural gradient method into the multilayer perceptrons (MLPs) and radial basis function (RBF) networks. We give a good performance for the Mackey–Glass chaotic time series prediction, and compare it with the LMA. Results show that the adaptive natural gradient methods for MLPs and RBFs, which are the online learning, can give almost the same performance with the LMA (MLPs), which is the batch mode learning. However, the performance of LMA (RBFs) is very poor and is very sensitive with the initial parameters.