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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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