On-line determination of ore type using cluster analysis and neural networks

被引：20
|
作者：
LAINE, S
LAPPALAINEN, H
JAMSA-JOUNELA, SL
机构：
[1] HELSINKI UNIV TECHNOL, DEPT COMP SCI, SF-02150 ESPOO, FINLAND
[2] HELSINKI UNIV TECHNOL, PROC CONTROL LAB, SF-02150 ESPOO, FINLAND
来源：
MINERALS ENGINEERING | 1995年 / 8卷 / 06期
关键词：
ARTIFICIAL INTELLIGENCE; EXPERT SYSTEMS; NEURAL NETWORKS; ORE TYPE CLASSIFICATION;
D O I：
10.1016/0892-6875(95)00026-M
中图分类号：
TQ [化学工业];
学科分类号：
0817 ;
摘要：
Expert systems have proved to be excellent tools for the control of mineral processes. An expert system based on on-line classification of the ore type has been designed and is described in. this paper The neural approach to computation has emerged in recent years for dealing with the sort of problems for which more conventional solutions have proven ineffective. In the study a comparison between the on-line cluster algorithm and the Kohonen feature map for ore type classification is presented. The study was carried out with measurement data from the Outokumpu Hitura mine.
引用
收藏
页码：637 / 648
页数：12
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