Soft-sensor Modeling of Product Particle Size in Ball Milling Circuits Based on Fuzzy Neural Networks with Particle Swarm Optimization

被引：0
|
作者：
Wu, Xinggang [1 ]
Yuan, Mingzhe [1 ]
机构：
[1] Chinese Acad Sci, Shenyang Inst Automat, Key Lab Ind Informat, Shenyang, Peoples R China
来源：
ICIA: 2009 INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION, VOLS 1-3 | 2009年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
By combining particle swarm optimization algorithm (PSO) with fuzzy neural networks (FNN), a PSO fuzzy neural networks (PSO-FNN) was proposed. Then PSO-FNN was applied in soft-sensor modeling of product particle size in ball milling circuits. The new method assumed that FNN was used to construct the soft-sensor modeling of product particle size while PSO was employed to optimize parameters of FNN. Experiment results show that the model based on PSO-FNN has higher precision and better performance than the model based on BPNN.
引用
收藏
页码：1433 / 1436
页数：4
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