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Adaptive inverse models find applications in
communication and magnetic channel equalization,
recovery of digital data and adaptive linearization of
sensor characteristics. In presence of outliers in the
training signal, the model accuracy is severely reduced. In
this paper three robust inverse models are developed by
recursively minimizing robust norms using BFO based
learning rule. The performance of these models is assesses
through simulation study and is compared with those
obtained by standard squared norm based models. It is in
general, observed that the Wilcoxon norm based model
provides best performance. Moreover the squared error
based model is observed to perform the worst.
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