Open AccessA Comparison Study on Shape Parameter Selection in Pattern Recognition by Radial Basis Function Neural Networks
Author(s) -
Sunisa Tavaen,
Sayan Kaennakham
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1921/1/012124
Subject(s) - radial basis function , selection (genetic algorithm) , pattern recognition (psychology) , artificial neural network , artificial intelligence , gaussian , basis (linear algebra) , shape parameter , radial basis function network , computer science , function (biology) , mathematics , statistics , physics , geometry , quantum mechanics , evolutionary biology , biology
This study investigates three choices of shape parameter selection when the so-called Radial Basis Function (RBF) is used. Under the problem of pattern recognition via RBF-Neural Network using RC-algorithm, three RBFs are focussed on; Gaussian (GA), Multiquadric (MQ), and Compactly-Supported (CS1). Two pattern recognition cases are tested and the best choice of shape parameter is validated using Model-Selection Criteria (MSC).