### 抄録

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their inter-relation of weights values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. Then we present a new learning method that trains the part of the weights and calculates the others by using these exact mathematical equations. This method often keeps a priori given mathematical structure exactly during the learning, in other words, training is done so that the network follows predetermined trajectory. Numerical computer simulation results are provided to support the present approaches.

元の言語 | English |
---|---|

ページ（範囲） | 29-35 |

ページ数 | 7 |

ジャーナル | Research Reports on Information Science and Electrical Engineering of Kyushu University |

巻 | 4 |

発行部数 | 1 |

出版物ステータス | Published - 1999 3 |

外部発表 | Yes |

### Fingerprint

### ASJC Scopus subject areas

- Electrical and Electronic Engineering
- Hardware and Architecture
- Engineering (miscellaneous)

### これを引用

*Research Reports on Information Science and Electrical Engineering of Kyushu University*,

*4*(1), 29-35.

**New learning method using prior information of neural networks.** / Lu, Baiquan; Hirasawa, Kotaro; Murata, Junichi; Furuzuki, Takayuki.

研究成果: Article

*Research Reports on Information Science and Electrical Engineering of Kyushu University*, 巻. 4, 番号 1, pp. 29-35.

}

TY - JOUR

T1 - New learning method using prior information of neural networks

AU - Lu, Baiquan

AU - Hirasawa, Kotaro

AU - Murata, Junichi

AU - Furuzuki, Takayuki

PY - 1999/3

Y1 - 1999/3

N2 - In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their inter-relation of weights values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. Then we present a new learning method that trains the part of the weights and calculates the others by using these exact mathematical equations. This method often keeps a priori given mathematical structure exactly during the learning, in other words, training is done so that the network follows predetermined trajectory. Numerical computer simulation results are provided to support the present approaches.

AB - In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering their inter-relation of weights values. Thus the training results are not usually good. The reason for this is that each parameter has its influence on others during the learning. To overcome this problem, first, we give exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. Then we present a new learning method that trains the part of the weights and calculates the others by using these exact mathematical equations. This method often keeps a priori given mathematical structure exactly during the learning, in other words, training is done so that the network follows predetermined trajectory. Numerical computer simulation results are provided to support the present approaches.

UR - http://www.scopus.com/inward/record.url?scp=0032653149&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032653149&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:0032653149

VL - 4

SP - 29

EP - 35

JO - Research Reports on Information Science and Electrical Engineering of Kyushu University

JF - Research Reports on Information Science and Electrical Engineering of Kyushu University

SN - 1342-3819

IS - 1

ER -