Judging the States of Blast Furnace by ART2 Neural Network

被引：0
|
作者：
Lin, Zhiling [1 ,2 ]
Yue, Youjun [1 ,2 ]
Zhao, Hui [1 ,2 ]
Li, Hongru [3 ]
机构：
[1] Tianjin Univ Technol, Sch Elect Engn & Automat, Tianjin 300384, Peoples R China
[2] Tianjin Key Lab Control Theory & Applicat Complic, Tianjin 300384, Peoples R China
[3] NorthEast Univ, Coll Informat Sci & Engn, Shenyang 110004, Peoples R China
来源：
SIXTH INTERNATIONAL SYMPOSIUM ON NEURAL NETWORKS (ISNN 2009) | 2009年 / 56卷
关键词：
PATTERNS;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An improved unsupervised neural network of ART2 is proposed to judge the pattern of blast furnace states. In this method six variables viz. charging speed, air flow, air temperature, air pressure, permeability indices and Si composition of liquid iron are determined to express the blast furnace states in a smelting process. The values of these variables are gained from the slide windows in order to overcome their time-varying difficulty. The pattern of blast furnace states is classified by ART2's competition learning and self steady mechanics. The simulation shows this method is effective.
引用
收藏
页码：857 / +
页数：2
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