A method for extracting approximate rules from neural network

Author

Narazaki, Hiroshi ; Shigaki, Ichiro ; Watanabe, Toshihiko

Author_Institution

Process Technol. Res. Lab., Kobe Steel Ltd., Japan

Volume

4

fYear

1995

fDate

20-24 Mar 1995

Firstpage

1865

Abstract

A knowledge acquisition method using a multilayered neural network (NN) is described. After training the NN for a data classification problem, our method extracts approximate classification rules from the NN; the method gives readability to the NN in which the information is represented in a distributed and unreadable manner. We show an application of our method to the standardization of operation conditions for a sintering process in an iron and steel making plant

Keywords

backpropagation; feedforward neural nets; knowledge acquisition; knowledge based systems; learning systems; pattern classification; process control; sintering; steel industry; uncertainty handling; approximate classification rules; approximate rule extraction; backpropagation; data classification; iron making plant; knowledge acquisition; machine learning; multilayered neural network; sintering process control; steel making plant; Data mining; Iron; Knowledge acquisition; Laboratories; Machine learning; Multi-layer neural network; Neural networks; Quantization; Standardization; Steel;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 1995. International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int

Conference_Location

Yokohama

Print_ISBN

0-7803-2461-7

Type

conf

DOI

10.1109/FUZZY.1995.409934

Filename

409934