Growing RBF structures using self-organizing maps

Qingyu Xiong, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

Research output: Contribution to conferencePaper

6 Citations (Scopus)

Abstract

We present a novel growing RBF network structure using SOM in this paper. It consists of SOM and RBF networks respectively. The SOM performs unsupervised learning and also the weight vectors belonging to its output nodes are transmitted to the hidden nodes in the RBF networks as the centers of RBF activation functions, as a result one to one correspondence relationship is realized between the output nodes in SOM and the hidden nodes in RBF networks. The RBF networks perform supervised training using delta rule. Therefore, the current output errors in the RBF networks can be used to determine where to insert a new SOM unit according to the rule. This also makes it possible to make the RBF networks grow until a performance criterion is fulfilled or until a desired network size is obtained. The simulations on the two-spirals benchmark are shown to prove the proposed networks have good performance.

Original languageEnglish
Pages107-111
Number of pages5
Publication statusPublished - 2000 Dec 1
Externally publishedYes
Event9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000 - Osaka, Japan
Duration: 2000 Sep 272000 Sep 29

Conference

Conference9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000
CountryJapan
CityOsaka
Period00/9/2700/9/29

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software

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  • Cite this

    Xiong, Q., Hirasawa, K., Hu, J., & Murata, J. (2000). Growing RBF structures using self-organizing maps. 107-111. Paper presented at 9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN2000, Osaka, Japan.