Accommodation to outliers in identification of non linear SISO systems with neural networks
Résumé
The problem of non-linear Single Input Single Output system identification in the presence of large errors in data is considered. Combining the capabilities of neural networks to solve non-linear problems by learning and a robust recursive prediction error learning rule based on the modeling of the errors, a new algorithm is drawn up. Its potential is illustrated through simulation studies.
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