We present a robust recursive total least squares (RRTLS) algorithm for multilayer feed-forward neural networks. So far, recursive least squares (RLS) has been successfully applied to training multilayer feed-forward neural networks. However, if input data has additive noise, the results from RLS could be biased. Theoretically, such biased results can be avoided by using the recursive total least squares (RTLS) algorithm based on Power Method. In this approach, Power Method uses rank-1 update. and thus is apt to be in ill condition. In this paper, therefore, we propose a robust RTLS algorithm using regularized UDU factorization. This method gives better performance than RLS based training over a wide range of SNRs.
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