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

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...

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Bibliographic Details
Published in:Journal of Central South University 2019, Vol.26 (1), p.43-62
Main Authors: Dai, Wei, Li, De-peng, Chen, Qi-xin, Chai, Tian-you
Format: Article
Language:English
Subjects:
Algorithms
Configurations
Engineering
Grinding
Hematite
Least squares method
Metallic Materials
Particle size
Robustness
Standard data
Weight
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Summary: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.
ISSN:2095-2899
2227-5223
DOI:10.1007/s11771-019-3981-2