Predicting sunspots using a layered perceptron neural network.

Details Predicting sunspots using a layered perceptron neural network. Predicting sunspots using a layered perceptron neural network.

Mar 1996

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1996itnn....7..501p&link_type=abstract

IEEE Trans. Neural Netw., Vol. 7, No. 2, p. 501 - 505

Statistics

Computation

Sunspots: Forecasts

Scientific paper

Interest in neural networks has expanded rapidly in recent years. Selecting the best structure for a given task, however, remains a critical issue in neural-network design. Although the performance of a network clearly depends on its structure, the procedure for selecting the optimal structure has not been thoroughly investigated, it is well known that the number of hidden units must be sufficient to discriminate each observation correctly. A large number of hidden units requires extensive computational time for training and often times prediction results may not be as accurate as expected. This study attempts to apply the principal component analysis to determine the structure of a multilayered neural network for time series forecasting problems. The main focus is to determine the number of hidden units for a multilayered feedforward network. One empirical experiment with sunspot data is used to demonstrate the usefulness of the proposed approach.

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