摘要
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.
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。
基金
Projects(61603393,61741318)supported in part by the National Natural Science Foundation of China
Project(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China
Project(2015M581885)supported by the Postdoctoral Science Foundation of China
Project(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
