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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2006

01.03.2006 | Original Article

Combined learning and pruning for recurrent radial basis function networks based on recursive least square algorithms

verfasst von: Chi Sing Leung, Ah Chung Tsoi

Erschienen in: Neural Computing and Applications | Ausgabe 1/2006

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Abstract

This paper discusses a way to combine training and pruning for the construction of a recurrent radial basis function network (RRBFN) based on recursive least square (RLS) learning. In our approach, a RRBFN is first trained using the proposed RLS algorithms. Afterwards, the error covariance matrix which is directly obtained from the RLS computations is used to remove some unimportant radial basis function (RBF) nodes. We propose two algorithms: (1) a “global” version which is suitable for low dimensional input space situation, and (2) a “local” version which can be applied in situations when the input dimension is large. In both cases, it is shown that the error covariance matrix, obtained from the RLS algorithms, can be used as a means for pruning the trained RRBFN. Simulation examples are presented to illustrate the proposed approaches.
Vorheriger Artikel An optimized Takagi-Sugeno type neuro-fuzzy system for modeling robot manipulators
Nächster Artikel Probabilistic ensemble simplified fuzzy ARTMAP for sonar target differentiation

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Metadaten
Titel
Combined learning and pruning for recurrent radial basis function networks based on recursive least square algorithms
verfasst von
Chi Sing Leung
Ah Chung Tsoi
Publikationsdatum
01.03.2006
Verlag
Springer Verlag
Erschienen in
Neural Computing and Applications / Ausgabe 1/2006
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-005-0009-7

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