Forecasting NOx Emissions in Power Plant Using Rough Set and QGA-based SVM

摘要：

NOx emissions prediction research is to the benefit of NOx emissions control. Studying the NOx emissions under new situation in coal-fired plant is of great significance. This paper introduces Quantum Genetic Algorithm (QGA) to optimize the parameters of SVM. Our experiment results demonstrate that using the QGA-SVM model will achieve better prediction than the individual SVM model.

关键词： NOx Emissions Rough Set Support Vector Machines (SVM) Quantum Genetic Algorithm (QGA)

作者: Jian-guo Zhou Yuan-yuan An

作者单位: School of Business AdministrationNorth China Electric Power UniversityBaoding, China, 071003 School of Business Administration North China Electric Power University Baoding, China, 071003

会议类型: 国际会议

会议名称: 2010 3rd International Conference on Advanced Computer Theory and Engineering(2010年第三届先进计算机理论与工程国际会议 ICACTE 2010)

会议地点: 成都

会议语种:英文

页码: 1-4

在线出版日期: 2010-08-20（万方平台首次上网日期，不代表论文的发表时间）