Identification of nonlinear black-box systems based on Universal Learning Networks

Jinglu Hu, Kotaro Hirasawa, Junichi Murata, Masanao Ohbayashi, Kousuke Kumamaru

研究成果: Paper査読

1 被引用数 (Scopus)

抄録

This paper presents a modeling scheme for nonlinear black-box systems based on Universal Learning Networks (ULN). The ULN, a superset of all kinds of neural networks, consists of two kinds of elements: nodes and branches corresponding to equations and their relations in traditional description of dynamic systems. Following the idea of ULN, a nonlinear black-box system is first represented by a set of related unknown equations, and then treated as the ULN with nodes and branches. Each unknown node function in the ULN is re-parameterized by using an adaptive fuzzy model. One of distinctive features of the black-box model constructed in this way is that it can incorporate prior knowledge obtained from input-output data into its modeling and thus its parameters to be trained have explicit meanings useful for estimation and application.

本文言語English
ページ2465-2470
ページ数6
出版ステータスPublished - 1998 1 1
外部発表はい
イベントProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
継続期間: 1998 5 41998 5 9

Other

OtherProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
CityAnchorage, AK, USA
Period98/5/498/5/9

ASJC Scopus subject areas

  • Software

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