Recurrent Sigma-Pi-linked back-propagation network |
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Authors: | T W S Chow Gou Fei |
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Affiliation: | (1) Department of Electronic Engineering, City Polytechnik of Honk-Kong, Tat Chee Avenue, Kowloon, Hong Kong |
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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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Keywords: | |
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