Universal Learning Networks with varying parameters

Kotaro Hirasawa*, Jinglu Hu, Junichi Murata, Chunzhi Jin, Hironobu Etoh, Hironobu Katagiri


研究成果: Paper査読

1 被引用数 (Scopus)


Universal Learning Network (ULN) which is a super-set of supervised learning networks has been already proposed. Parameters in ULN are trained in order to optimize a criterion function as conventional neural networks, and after training they are used as constant parameters. In this paper, a new method to alter the parameters depending on the network flows is presented to enhance representation abilities of networks. In the proposed method, there exists two kinds of networks, the first one is a basic network which includes varying parameters and the other one is a network which calculates the optimal varying parameters depending on the network flows of the basic network. It is also proposed in this paper that any type of networks such as fuzzy inference networks, radial basis function networks and neural networks can be used for the basic and parameter calculation networks. From simulations where parameters in a neural network are altered by a fuzzy inference networks, it is shown that the networks with the same number of varying parameters have higher representation abilities than the conventional networks.

出版ステータスPublished - 1999
イベントInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
継続期間: 1999 7月 101999 7月 16


OtherInternational Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能


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