A novel algorithm to configure RBF networks

I. Sohn, N. Ansari

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


The most important factor in configuring an optimum radial basis function (RBF) network is the appropriate selection of the number of neural units in the hidden layer. This paper proposes a novel algorithm called the scattering-based clustering (SBC) algorithm, in which the frequency sensitive competitive learning (FSCL) algorithm is first applied to let the neural units converge. Scatter matrices of the clustered data are then used to compute the sphericity for each k, where k is the number of clusters. The optimum number of neural units to be used in the hidden layer is then obtained. A comparative study is done between the SBC algorithm and rival penalizes competitive learning (RPCL) algorithm, and the result shows that the SBC algorithm outperforms other algorithms such as CL, FSCL, and RPCL.

Original languageEnglish (US)
Title of host publication1997 IEEE International Conference on Neural Networks, ICNN 1997
Number of pages6
StatePublished - 1997
Event1997 IEEE International Conference on Neural Networks, ICNN 1997 - Houston, TX, United States
Duration: Jun 9 1997Jun 12 1997

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


Other1997 IEEE International Conference on Neural Networks, ICNN 1997
Country/TerritoryUnited States
CityHouston, TX

All Science Journal Classification (ASJC) codes

  • Software


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