Abstract
The problem of identification for nonlinear SISO systems in the presence of outliers in data is considered. Neural networks are used for their capabilities to solve nonlinear problems by learning. Three prediction error learning rules based on outlier-robust criteria are drawn up, for batch and recursive identification. The robust recursive algorithms are compared with the standard Levenberg-Marquardt update rule through a simulation example of fault detection.
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