FIR and IIR Synapses, a New Neural Network Architecture for Time Series Modeling

A new neural network architecture involving either local feedforward global feedforward, and/or local recurrent global feedforward structure is proposed. A learning rule minimizing a mean square error criterion is derived. The performance of this algorithm (local recurrent global feedforward archite...

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Bibliographic Details
Published in:Neural computation 1991-09, Vol.3 (3), p.375-385
Main Authors: Back, A. D., Tsoi, A. C.
Format: Article
Language:English
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Summary:A new neural network architecture involving either local feedforward global feedforward, and/or local recurrent global feedforward structure is proposed. A learning rule minimizing a mean square error criterion is derived. The performance of this algorithm (local recurrent global feedforward architecture) is compared with a local-feedforward global-feedforward architecture. It is shown that the local-recurrent global-feedforward model performs better than the local-feedforward global-feedforward model.
ISSN:0899-7667
1530-888X
DOI:10.1162/neco.1991.3.3.375