Chaotic time-series prediction using resource-allocating RBF networks

Abstract

One of the main problems associated with artificial neural networks on-line learning methods is the estimation of model order. In this paper, we report about a new approach to constructing a resource-allocating network exploiting weights adaptation using QRD-based recursive least-squares technique. Further, we studied the performance of Dynamic Cell Structures algorithm for on-line adaptation of centers positions. The proposed method was tested on the task of Mackey-Glass time-series prediction. Order of resulting networks and their prediction abilities were superior to those previously reported by Platt.


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