Prediction of Chaotic Time-Series with a Resource-Allocating RBF Network

Abstract. One of the main problems associated with arti cial neural networks online learning methods is the estimation of model order. In this paper, we report about a new approach to constructing a resource-allocating radial basis function network exploiting weights adaptation using recursive least-squares technique based on Givens QR decomposition. Further, we study the performance of pruning strategy we introduced to obtain the same prediction accuracy of the network with lower model order. The proposed methods were tested on the task of Mackey-Glass timeseries prediction. Order of resulting networks and their prediction performance were superior to those previously reported by Platt 12]. Key words: Givens QR decomposition, on-line learning, resource-allocating RBF network, time-series prediction

• Type: Journal
• Year: 1998
• Where: NPL
• Authors: Roman Rosipal, Milos Koska, Igor Farkas
• Keywords: Model Order, NPL 1998, QR Decomposition, Radial Basis Function Network