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 configu...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.展开更多
This paper considers experimental situations where the interested effects have to be or- thogonal to a set of nonnegligible effects. It is shown that various types of orthogonal arrays with mixed strength are A-optima...This paper considers experimental situations where the interested effects have to be or- thogonal to a set of nonnegligible effects. It is shown that various types of orthogonal arrays with mixed strength are A-optimal for estimating the parameters in ANOVA high dimension model representation. Both cases including interactions or not are considered in the model. In particularly, the estimations of all main effects are A-optimal in a mixed strength （2, 2）3 orthogonal array and the estimations of all main effects and two-factor interactions in G~ x G~ are A-optimal in a mixed strength （2, 2）4 orthogonal array. The properties are also illustrated through a simulation study.展开更多
基金Projects(61603393,61741318)supported in part by the National Natural Science Foundation of ChinaProject(BK20160275)supported by the Natural Science Foundation of Jiangsu Province,China+1 种基金Project(2015M581885)supported by the Postdoctoral Science Foundation of ChinaProject(PAL-N201706)supported by the Open Project Foundation of State Key Laboratory of Synthetical Automation for Process Industries of Northeastern University,China
文摘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.
基金the National Natural Science Foundation of China under Grant Nos.11171065,11301073the Natural Science Foundation of Jiangsu under Grant No.BK20141326+1 种基金the Research Fund for the Doctoral Program of Higher Education of China under Grant No.20120092110021Scientific Research Foundation of Graduate School of Southeast University under Grant No.YBJJ1444
文摘This paper considers experimental situations where the interested effects have to be or- thogonal to a set of nonnegligible effects. It is shown that various types of orthogonal arrays with mixed strength are A-optimal for estimating the parameters in ANOVA high dimension model representation. Both cases including interactions or not are considered in the model. In particularly, the estimations of all main effects are A-optimal in a mixed strength （2, 2）3 orthogonal array and the estimations of all main effects and two-factor interactions in G~ x G~ are A-optimal in a mixed strength （2, 2）4 orthogonal array. The properties are also illustrated through a simulation study.
