A hybrid least square support vector machine for boiler efficiency prediction

摘要：

　　A hybrid least square support vector machine(LSSVM)is proposed to predict the boiler combustion efficiency.In this approach,a principal component analysis(PCA)is employed to reconstruct new variables as the input of the predictive model.Then,a particle swarm optimization(PSO)algorithm optimized LSSVM is proposed.The parameters of LSSVM are optimized dynamically by PSO and the output value of the model is corrected to improve the prediction accuracy.The experimental results based on practical data set illustrate that the proposed hybrid LSSVM obtains better accuracy compared with other data-driven approaches,such as the multi-layer perceptron(MLP)and Elman neural network.The proposed boiler combustion efficiency model can meet the requirements of boiler control and optimization.

关键词： Boiler combustion efficiency PCA least squares support vector machine Particle swarm optimization Model correction

作者: Xiaoyan Wu Zhenhao Tang Shengxian Cao

作者单位: School of Automation Engineering,Northeast Electric Power University Jilin,China

会议类型: 国际会议

会议名称: 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference(ITOEC2017)(2017 IEEE 第3届信息技术与机电一体化工程国际学术会议)

会议地点: 重庆

会议语种:英文

页码: 1202-1205

在线出版日期: 2017-10-03（万方平台首次上网日期，不代表论文的发表时间）