基于最小二乘支持向量机和PSO算法的电厂烟气含氧量软测量

SOFT MEASUREMENT OF OXYGEN CONTENT IN FLUE GAS BASED ON LEAST SQUARE SUPPORT VECTOR MACHINE AND PSO ALGORITHM

由于最小二乘支持向量机有2个必需确定的参数,多参数调节成为最小二乘支持向量机较难解决的问题。将粒子群算法应用在多参数优化中,解决了多参数优化问题。将这一结果应用在电厂烟气含氧量软测量中,取得了较好的效果。

Because of the least square support vector machine （LS -SVM） has two parameters to be determined, the multi- parameter regulation becomes a more difficult problem to be solved by the LS - SVM. The particale stream organ（PSO） algorithm has been used in the multi - parameter optimization, solving the problem of multi - parameter optimization. This theoritical result has been used in the soft measurement of oxygen content in flue gas of thermal power plant, obtaining better effectiveness.