Study and Application of Fuzzy Neural Network Model of Improved Training Method to Predict End Point Phosphorus

刘冬梅; 陈斌; 王淑阁; 邹宗树; 余艾冰

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Study and Application of Fuzzy Neural Network Model of Improved Training Method to Predict End Point Phosphorus

更新时间：2026-01-21

- Study and Application of Fuzzy Neural Network Model of Improved Training Method to Predict End Point Phosphorus
- Special Steel Vol. 28, Issue 2, Pages: 41-43(2007)
- 作者机构：
  
  东北大学材料与冶金学院，沈阳4,H000
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Received：26 August 2006，
  
  Online First：10 March 2023，
  
  Published：10 March 2007
- 稿件说明：
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刘冬梅, 陈斌, 王淑阁, et al. Study and Application of Fuzzy Neural Network Model of Improved Training Method to Predict End Point Phosphorus[J]. Special Steel, 2007, 28(2): 41-43.
DOI：

刘冬梅, 陈斌, 王淑阁, et al. Study and Application of Fuzzy Neural Network Model of Improved Training Method to Predict End Point Phosphorus[J]. Special Steel, 2007, 28(2): 41-43. DOI：

摘要

根据150 t转炉的冶炼工艺和生产数据及转炉终点磷含量的影响因素

并针对现有BP网络学习算法的不足

基于BP算法提出一种改进网络训练算法

建立了基于模糊神经网络的转炉终点磷含量的预报模型。结果表明

改进后的模型预报转炉终点磷含量误差为±0．002％的命中率达68．69％

预报误差±0．004％的命中率达95．96％

磷含量的最大误差为±0．006％。

Abstract

Back-propagation based improved training network algorithm was proposed and a prediction model for converter end point phosphorus based on fuzzy neural network has been established based on melting process and production data of a 150t converter and analysis on influence factors on end point phosphorus

in accordance with the deficiencies of present back propagation algorithm. The results showed that percentage of hits of converter end point phosphorus content in steel with error ±0. 002% was 68. 69%

that with error 士 0. 004% was up to 95. 96%

and the maximum error of end point phosphorus content was ±0. 006%

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