An efficient recursive total least squares algorithm for training multilayer feedforward neural networks

Authors:
Nakjin Choi;JunSeok Lim;KoengMo Sung
Affiliations:
School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea;Department of Electronics Engineering, Sejong University, Seoul, Korea;School of Electrical Engineering and Computer Science, Seoul National University, Seoul, Korea
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

We present a recursive total least squares (RTLS) algorithm for multilayer feedforward neural networks. So far, recursive least squares (RLS) has been successfully applied to training multilayer feedforward neural networks. If the input data contains additive noise, the results from RLS could be biased. Such biased results can be avoided by using the RTLS algorithm. The RTLS algorithm described in this paper performs better than RLS algorithm over a wide range of SNRs and involves approximately the same computational complexity of O(N2) as the RLS algorithm.