摘要
提出了同时利用自适应加权融合和最小二乘支持向量机建模的实用新方法。首先,给出了基于小波的自适应加权融合和最小二乘支持向量机算法;其次,将BP神经网络、最小二乘支持向量机和基于小波的自适应加权融合的最小二乘支持向量机算法进行建模精度比较;最后,采用真实火电厂飞灰含碳量数据进行模型验证与预测,仿真结果表明基于小波的自适应加权融合的最小二乘支持向量机算法具有较好的建模精度和实用性。
A novel-modeling algorithm,which is based on adaptive weighted fusion and least square support vector machine （LSSVM）,is proposed.Firstly,Adaptive weighted fusion based on wavelet and LSSVM algorithms are designed.Secondly,BP neural network,LSSVM and LSSVM based on wavelet and adaptive weighted fusion algorithms are used to modeling.Finally,for real data of the carbon content in fly ash for power plant,the simulation results show that LSSVM based on wavelet and adaptive weighted fusion algorithm has better precision of the modeling and practicability.
出处
《自动化与仪器仪表》
2010年第6期112-115,共4页
Automation & Instrumentation
基金
邯郸市科学技术局资助项目(0824201092-3)
