## 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 |
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ページ | 2465-2470 |

ページ数 | 6 |

出版ステータス | Published - 1998 1月 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月 4 → 1998 5月 9 |

### Other

Other | Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) |
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City | Anchorage, AK, USA |

Period | 98/5/4 → 98/5/9 |

## ASJC Scopus subject areas

- ソフトウェア