A functions localized neural network with branch gates

Qingyu Xiong, Kotaro Hirasawa, Jinglu Hu, Junichi Murata

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


In this paper, a functions localized network with branch gates (FLN-bg) is studied, which consists of a basic network and a branch gate network. The branch gate network is used to determine which intermediate nodes of the basic network should be connected to the output node with a gate coefficient ranging from 0 to 1. This determination will adjust the outputs of the intermediate nodes of the basic network depending on the values of the inputs of the network in order to realize a functions localized network. FLN-bg is applied to function approximation problems and a two-spiral problem. The simulation results show that FLN-bg exhibits better performance than conventional neural networks with comparable complexity.

Original languageEnglish
Pages (from-to)1461-1481
Number of pages21
JournalNeural Networks
Issue number10
Publication statusPublished - Dec 2003

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence


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