Universal Learning Networks with varying parameters

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

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.

ホスト出版物のタイトルProceedings of the International Joint Conference on Neural Networks
出版ステータス出版済み - 1999
イベントInternational Joint Conference on Neural Networks (IJCNN'99) - Washington, DC, USA
継続期間: 7 10 19997 16 1999


その他International Joint Conference on Neural Networks (IJCNN'99)
CityWashington, DC, USA

All Science Journal Classification (ASJC) codes

  • Software

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