Journal article

Prediction intervals based soft sensor development using fuzzy information granulation and an improved recurrent ELM

Y Xu, H Jiang, W Zhang, A Rajabifard, N Chen, Y Chen, Y He, Q Zhu

Chemometrics and Intelligent Laboratory Systems | Elsevier BV | Published : 2019

Abstract

With the increasing complexity of large-scale industrial production processes, the number of variable factors is increasing. As a result, it is demanding to predict process key variables accurately. Currently, most of soft sensor models using support vector regression and artificial neural networks are based on point prediction. The soft measurement models using the technique of point prediction can only track or fit set values. It is difficult to deal with the problem of system uncertainty and to make reliability analysis using the point prediction based soft sensors. To address this problem, this paper proposes a development method of soft sensor using the technique of prediction intervals..

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Grants

Awarded by National Natural Science Foundation of China


Awarded by China Scholarship Council State -Sponsored Scholarship Program


Awarded by Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WUHAN University


Awarded by Fundamental Research Funds for the Central Universities


Funding Acknowledgements

This work is supported by grants from the National Natural Science Foundation of China under Grant Nos.61573051 and 61703027, the China Scholarship Council State-Sponsored Scholarship Program (Grant No. 201806885004), and the Open Research Fund of State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, WUHAN University (Grant No.18I01), the Fundamental Research Funds for the Central Universities under Grant Nos. XK1802-4 and JD1914.