A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity

Patricia Crowther, Robert Cox, Dharmendra Sharma

Research output: A Conference proceeding or a Chapter in BookConference contribution

4 Citations (Scopus)

Abstract

Neural networks are increasingly used in a wide variety of applications such as speech recognition, diagnostic prediction, income prediction and credit screening. This paper empirically compares the performance of Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks using artificially generated data sets, enabling us to accurately chart the effectiveness of each network type and to provide some guidance to practitioners as to which type of network to use with their data. We find that when the discriminator is simple, RBF and MLP network performances are similar; when the number of data points is relatively small the MLP outperforms the RBF; when the discriminator is complex the RBF outperforms the MLP; and when the data has an unrelated input and the underlying discriminator is simple, the MLP outperforms the RBF
Original languageEnglish
Title of host publicationInternational Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004
Subtitle of host publicationKnowledge-Based Intelligent Information and Engineering Systems
EditorsMircea Gh Negoita, Robert J Howlett, Lakhmi C Jain
Place of PublicationBerlin, Germany
PublisherSpringer
Pages210-216
Number of pages7
ISBN (Print)9783540232056
DOIs
Publication statusPublished - 2004
Event8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004 - Wellington, New Zealand
Duration: 20 Sep 200425 Sep 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3215
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2004
CountryNew Zealand
CityWellington
Period20/09/0425/09/04

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Cite this

Crowther, P., Cox, R., & Sharma, D. (2004). A Study of the Radial Basis Function Neural Network Classifiers using Known Data of Varying Accuracy and Complexity. In M. G. Negoita, R. J. Howlett, & L. C. Jain (Eds.), International Conference on Knowledge-Based and Intelligent Information and Engineering Systems KES 2004: Knowledge-Based Intelligent Information and Engineering Systems (pp. 210-216). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3215). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-540-30134-9_30