热力发电2017，Vol.46Issue(7)：79-85,7.DOI:10.3969/j.issn.1002-3364.2017.07.079
基于在线自适应的鲁棒最小二乘支持向量机及其应用
Robust least squares support vector machine based on online adaptive and its application
金秀章 1刘潇1
作者信息
- 1. 华北电力大学控制与计算机工程学院,河北保定 071003
- 折叠
摘要
Abstract
Aiming at solving the problems of the least squares support vector machine (LSSVM) occurred during on-site data modeling, such as being difficult to meet the distinct operating conditions poor robustness, a robust LSSVM model based on online adaptive revision (online-RLSSVM) was proposed. This method uses the total forecast error as the threshold value, adaptively updates the model parameters according to different working conditions, which improves the adaptability of the model to the data. At the same time, the fuzzy membership gives fuzzy dynamic weights to the square error term in the vector machine optimization, to enhance the anti-noise ability of the robust LSSVM model. Furthermore, this method was applied to predict the primary airflow in power plant and the results were compared with that of the ordinary LSSVM model. The results show that, the established model has better robustness and higher prediction accuracy. This algorithm can be used for real-time prediction and estimation of data under different operating conditions, and the research provides good data support for various on-line monitoring systems.关键词
在线自适应控制/鲁棒性/最小二乘支持向量机/模糊隶属度/一次风量/软测量Key words
online adaptive control/robust/LSSVM/fuzzy membership/primary air volume/soft measurement分类
信息技术与安全科学引用本文复制引用
金秀章,刘潇..基于在线自适应的鲁棒最小二乘支持向量机及其应用[J].热力发电,2017,46(7):79-85,7.
