## 抄録

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.

本文言語 | 英語 |
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ホスト出版物のタイトル | Proceedings of the International Joint Conference on Neural Networks |

出版社 | IEEE |

ページ | 1302-1307 |

ページ数 | 6 |

巻 | 2 |

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 1999 → 7 16 1999 |

### その他

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

Period | 7/10/99 → 7/16/99 |

## All Science Journal Classification (ASJC) codes

- Software