New training method and optimal structure of backpropagation networks

Authors:
Songyot Sureerattanan;Nidapan Sureerattanan
Affiliations:
Information Technology Department, Bank of Thailand, Pranakorn, Bangkok, Thailand;Department of Computer Education, Faculty of Technical Education, King Mongkut's Institute of Technology North Bangkok, Bangsue, Bangkok, Thailand
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Year:
2005

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Abstract

New algorithm was devised to speed up the convergence of backpropagation networks and the Bayesian Information Criterion was presented to obtain the optimal network structure. Nonlinear neural network problem can be partitioned into the nonlinear part in the weights of the hidden layers and the linear part in the weights of the output layer. We proposed the algorithm for speeding up the convergence by employing the conjugate gradient method for the nonlinear part and the Kalman filter algorithm for the linear part. From simulation experiments with daily data on the stock prices in the Thai market, it was found that the algorithm and the Bayesian Information Criterion could perform satisfactorily.