摘要
针对电厂烟气含氧量难以进行有效预测的问题,从提高烟气含氧量预测模型在线自适应能力的角度出发,提出一种基于即时学习策略的改进支持向量机建模方法,并基于电厂实际运行数据进行了仿真研究。仿真实验表明,与标准BP神经网络和支持向量机等建模方法的比较,本算法具有更好的预测性能,虽然计算开销有所增加,但能够满足锅炉燃烧过程烟气含氧量预测的实时性要求。
Based on the fact that the power plant flue gas oxygen content is hard to predict effectively, an improved support vector machine modeling method based on just-in-time learning is proposed form improving the online self-adaptive ability of the prediction model. In addition, the simulation experiments are carried out based on the actual operation data. Simulation results show that compared with the standard BP neural network and the standard SVM prediction model, although the computing cost is increased, the proposed prediction model has better prediction performance and can satisfy the real-time requirements for the flue gas oxygen content in the boiler combustion process.
出处
《装备制造技术》
2010年第4期47-51,共5页
Equipment Manufacturing Technology
基金
湖南省科学技术与科技计划(2006GK3130)
湖南省自然科学基金资助项目(05JJ30121)
关键词
烟气含氧量
在线自适应
即时学习
改进支持向量机
flue gas oxygen content
online adaptive
just-in-time learning
improved support vector machine
