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Journal of Central South University

, Volume 26, Issue 1, pp 43–62 | Cite as

Data driven particle size estimation of hematite grinding process using stochastic configuration network with robust technique

  • Wei Dai (代伟)Email author
  • De-peng Li (李德鹏)
  • Qi-xin Chen (陈其鑫)
  • Tian-you Chai (柴天佑)
Article
  • 9 Downloads

Abstract

As a production quality index of hematite grinding process, particle size (PS) is hard to be measured in real time. To achieve the PS estimation, this paper proposes a novel data driven model of PS using stochastic configuration network (SCN) with robust technique, namely, robust SCN (RSCN). Firstly, this paper proves the universal approximation property of RSCN with weighted least squares technique. Secondly, three robust algorithms are presented by employing M-estimation with Huber loss function, M-estimation with interquartile range (IQR) and nonparametric kernel density estimation (NKDE) function respectively to set the penalty weight. Comparison experiments are first carried out based on the UCI standard data sets to verify the effectiveness of these methods, and then the data-driven PS model based on the robust algorithms are established and verified. Experimental results show that the RSCN has an excellent performance for the PS estimation.

Key words

hematite grinding process particle size stochastic configuration network robust technique M-estimation nonparametric kernel density estimation 

基于鲁棒随机配置网络的赤铁矿磨矿过程数据驱动粒度估计

摘要

粒度作为赤铁矿磨矿过程的关键生产质量指标,针对其难以实时检测的问题,本文在随机配置 网络(Stochastic configuration network, SCN)的基础上,证明了一种基于加权最小二乘的鲁棒 SCN(Robust SCN, RSCN)的万能逼近特性,并分别采用Huber 损失函数的M 估计、四分位距(Inter quartile range, IQR)的M 估计和非参数核密度估计(Nonparametric kernel density estimation, NKDE) 三个函数计算惩罚权值,从而提出三种RSCN 算法,在UCI 标准数据集上的实验研究表明了所提算 法的有效性。基于RSCN 算法建立了数据驱动的赤铁矿磨矿过程粒度模型,取得了良好的估计效果。

关键词

赤铁矿磨矿过程 粒度 随机配置网络(SCN) 鲁棒技术 M 估计 非参数核密度估计(NKDE) 

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Copyright information

© Central South University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information and Control EngineeringChina University of Mining and TechnologyXuzhouChina
  2. 2.State Key Laboratory of Synthetical Automation for Process IndustriesNortheastern UniversityShenyangChina

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