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Optimal learning for patterns classification in RBF networks


Hoang, TA and Nguyen, DT, Optimal learning for patterns classification in RBF networks, Electronics Letters, 38, (20) pp. 1188-1190. ISSN 0013-5194 (2002) [Refereed Article]

DOI: doi:10.1049/el:20020822


The proposed modifying of the structure of the radial basis function (RBF) network by introducing the weight matrix to the input layer (in contrast to the direct connection of the input to the hidden layer of a conventional RBF) so that the training space in the RBF network is adaptively separated by the resultant decision boundaries and class regions is reported. The training of this weight matrix is carried out as for a single-layer perceptron together with the clustering process. This way the network is capable of dealing with complicated problems, which have a high degree of interference in the training data, and achieves a higher classification rate over the current classifiers using RBF.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Control engineering, mechatronics and robotics
Research Field:Field robotics
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Hoang, TA (Mr Tuan Hoang)
UTAS Author:Nguyen, DT (Professor Thong Nguyen)
ID Code:24575
Year Published:2002
Web of Science® Times Cited:2
Deposited By:Engineering
Deposited On:2002-08-01
Last Modified:2003-05-05

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