摘要
针对最小二乘支持向量机(LSSVM)在利用现场数据建模时难以适应不同工况,鲁棒性较差的问题,提出了一种基于在线自适应修正的鲁棒LSSVM模型。该方法以总的预报误差大小作为阈值,根据不同工况自适应更新参数,从而提高模型对数据的适应性;同时采用模糊隶属度对向量机优化问题中的误差平方项赋予动态权值,增强模型的抗噪声能力。将该方法应用于电厂实际数据对一次风量的预测,并与普通LSSVM模型相比,结果表明该算法所建立的模型鲁棒性强、预测精度高。该模型可满足不同工况下数据的实时预测和估计,为各种在线监测系统提供了良好的数据支持。
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.
作者
金秀章
刘潇
JIN Xiuzhang LIU Xiao(School of Control and Computer Engineering, North China Electric Power University, B aoding 071003, China)
出处
《热力发电》
CAS
北大核心
2017年第7期79-85,共7页
Thermal Power Generation
