Size-reducing RBF networks

Junichi Murata, Shinji Itoh, Kotaro Hirasawa

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


In this paper, a new approach is proposed to reduce the complexity of radial basis function (RBF) networks. This approach starts with an enough number of hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in the problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to either of classification problems and function approximation problems.

Original languageEnglish
Number of pages5
Publication statusPublished - 1999
EventInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
Duration: Jul 10 1999Jul 16 1999


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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