A Radial Basis Function network training algorithm using a non-symmetric partition of the input space

 
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2011 (EN)
A Radial Basis Function network training algorithm using a non-symmetric partition of the input space (EN)

Σαρίμβεης, Χαράλαμπος Κ. (EL)
Αλεξανδρίδης, Αλέξανδρος Π. (EL)
Νίνος, Κωνσταντίνος Δ. (EL)

Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε. (EL)

This work presents the non-symmetric fuzzy means algorithm which is a new methodology for training Radial Basis Function neural network models. The method is based on a non-symmetric fuzzy partition of the space of input variables which results to networks with smaller structures and better approximation capabilities compared to other state-of-the-art training procedures. The lower modeling error and the smaller size of the produced models become particularly important when they are used in online applications. This is demonstrated by integrating the model produced by the proposed algorithm in a Model Predictive Control configuration, resulting in better control performance and shorter computational times. (EN)

journalArticle

ασαφής κατανομή (EN)
Fuzzy partition (EN)
Radial basis functions (EN)
ασαφή μέσα (EN)
continuous stirred tank reactor (EN)
νευρωνικά δίκτυα (EN)
Non-symmetric partition (EN)
μοντέλο πρόβλεψης ελέγχου (EN)
ακτινική συνάρτηση βάσης (EN)
μη συμμετρική κατανομή (EN)
Fuzzy means (EN)
Mackey-Glass (EN)
Neural networks (Computer science) (EN)
Predictive control (EN)
συνεχής ανάδευση δεξαμενής αντιδραστήρα (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

Advances in Engineering Software (EN)

English

2011-10

DOI: 10.1016/j.advengsoft.2011.05.026

N/A (EN)



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