Size-reducing RBF networks

Junichi Murata, Shinji Itoh, Kotaro Hirasawa

研究成果: Contribution to conferencePaper査読

1 被引用数 (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.

出版ステータス出版済み - 12 1 1999
イベントInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
継続期間: 7 10 19997 16 1999


その他International Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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