Clustering LS-SVM models for the prediction of unburned carbon content in fly ash

This paper investigates factors that influences the unburned carbon content in fly ash and selects the optical factors from the original characteristics by means of minimal-redundancy-maximal-relevance criterion (mRMR). And on this basis, this paper proposes a novel model called clustering least squ...

Full description

Saved in:
Bibliographic Details
Published inThe 27th Chinese Control and Decision Conference (2015 CCDC) pp. 19 - 24
Main Authors Weijing Shi, Jingcheng Wang, Yuanhao Shi, Zhengfeng Liu
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
Boilers
Carbon
Clustering least squares support vector machine
Data models
Fuzzy C-means
mRMR
Mutual information
Prediction algorithms
Predictive models
Unburned carbon content
Valves
Online AccessGet full text

Cover

More Information
Summary:This paper investigates factors that influences the unburned carbon content in fly ash and selects the optical factors from the original characteristics by means of minimal-redundancy-maximal-relevance criterion (mRMR). And on this basis, this paper proposes a novel model called clustering least squares support vector machine (CLS-SVM) to predict the unburned carbon content in fly ash. In this CLS-SVM model, a fuzzy c-means cluster algorithm (FCM) is adopted to decompose the original data into three different sub data sets. Taking advantage of both theory of clustering algorithm and advanced statistical learning methodology, CLS-SVM models are built specifically for each different sub data sets. Then the CLS-SVM models are developed to predict the key parameter - unburned carbon content, which is verified through operation data of a 300MW generating unit.
ISSN:1948-9439
1948-9447
DOI:10.1109/CCDC.2015.7161660