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Article Dans Une Revue Neurocomputing Année : 1997

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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hal-00951969 , version 1 (25-02-2014)

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Gérard Bloch, Philippe Thomas, Didier Theilliol. Accommodation to outliers in identification of non linear SISO systems with neural networks. Neurocomputing, 1997, 14 (1), pp.85-99. ⟨10.1016/0925-2312(95)00134-4⟩. ⟨hal-00951969⟩
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