Universal Learning Networks with Branch Control

Kotaro Hirasawa, Jinglu Hu, Qingyu Xiong, Junichi Murata, Yuhki Shiraishi

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)


In this paper, Universal Learning Networks with Branch Control (BrcULNs) are proposed, which consist of basic networks and branch control networks. The branch control network can be used to determine which branches of the basic network should be connected or disconnected. This determination depends on the inputs or the network flows of the basic network. Therefore, by using the BrcULNs, locally functions distributed networks can be realized depending on the values of the inputs of the network or the information of the network flows. The proposed network is applied to some function approximation problems. The simulation results show that the BrcULNs exhibit better performance than the conventional networks with comparable complexity.

Original languageEnglish
Number of pages6
Publication statusPublished - Jan 1 2000
EventInternational Joint Conference on Neural Networks (IJCNN'2000) - Como, Italy
Duration: Jul 24 2000Jul 27 2000


OtherInternational Joint Conference on Neural Networks (IJCNN'2000)
CityComo, Italy

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence


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