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Optimal learning for patterns classification in RBF networks

journal contribution
posted on 2023-05-16, 13:37 authored by Hoang, TA, Nguyen, T
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.

History

Publication title

Electronics Letters

Volume

38

Issue

20

Pagination

1188-1190

ISSN

0013-5194

Department/School

School of Engineering

Publisher

The Institution of Electrical Engineers Publishing Department

Place of publication

UK

Repository Status

  • Restricted

Socio-economic Objectives

Energy systems and analysis

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