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.
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Chow, T.W.S., Fei, G. Recurrent Sigma-Pi-linked back-propagation network. Neural Process Lett 1, 5–8 (1994). https://doi.org/10.1007/BF02310935
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DOI: https://doi.org/10.1007/BF02310935