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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2009

01.09.2009 | Original Article

Neural network training with optimal bounded ellipsoid algorithm

verfasst von: José de Jesús Rubio, Wen Yu, Andrés Ferreyra

Erschienen in: Neural Computing and Applications | Ausgabe 6/2009

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Abstract

Compared to normal learning algorithms, for example backpropagation, the optimal bounded ellipsoid (OBE) algorithm has some better properties, such as faster convergence, since it has a similar structure as Kalman filter. OBE has some advantages over Kalman filter training, the noise is not required to be Guassian. In this paper OBE algorithm is applied in training the weights of the feedforward neural network for nonlinear system identification. Both hidden layers and output layers can be updated. From a dynamic system point of view, such training can be useful for all neural network applications requiring real-time updating of the weights. Two simulations give the effectiveness of the suggested algorithm.
Vorheriger Artikel Face recognition using Gabor-based complete Kernel Fisher Discriminant analysis with fractional power polynomial models
Nächster Artikel An stable online clustering fuzzy neural network for nonlinear system identification

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Metadaten
Titel
Neural network training with optimal bounded ellipsoid algorithm
verfasst von
José de Jesús Rubio
Wen Yu
Andrés Ferreyra
Publikationsdatum
01.09.2009
Verlag
Springer-Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 6/2009
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-008-0203-5

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