Recurrent Sigma-Pi-linked back-propagation network

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Original languageEnglish
Pages (from-to)5-8
Journal / PublicationNeural Processing Letters
Volume1
Issue number2
Publication statusPublished - Jun 1994

Abstract

A recurrent Sigma-Pi-linked back-propagation neural network is presented. The increase of input information is achieved by the introduction of "higher-order≓ terms, that are generated through functional-linked input nodes. Based on the Sigma-Pi-linked model, this network is capable of approximating more complex function at a much faster convergence rate. This recurrent network is intensively tested by applying to different types of linear and nonlinear time-series. Comparing to the conventional feedforward BP network, the training convergence rate is substantially faster. Results indicate that the functional approximation property of this recurrent network is remarkable for time-series applications. © 1994 Kluwer Academic Publishers.