Evaluation of multi-layered RBF networks

Kotaro Hirasawa, Takuya Matsuoka, Masanao Ohbayashi, Junichi Murata

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


In this paper, an investigation into the performance of multi-layered Radial Basis Functions(RBF) networks is conducted which use Gaussian function in place of sigmoidal function in multi-layered Neural Networks(NNs). The focus is on the difference of approximation abilities between multi-layered RBF networks and NNs. A function approximation problem is employed to evaluate the performance of multi-layered RBF networks, and several types of different functions are used as the functions to be approximated. Gradient method is employed to optimize the parameters including centers, widths, and linear connection weights to the output nodes. It is shown from the result that RBF does not always have significant advantages over sigmoidal functions when they are used in multi-layered networks.

Original languageEnglish
Pages (from-to)908-911
Number of pages4
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Publication statusPublished - Dec 1 1997
EventProceedings of the 1997 IEEE International Conference on Systems, Man, and Cybernetics. Part 1 (of 5) - Orlando, FL, USA
Duration: Oct 12 1997Oct 15 1997

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Hardware and Architecture


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