会议专题

A hybrid least square support vector machine for boiler efficiency prediction

  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.

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

Xiaoyan Wu Zhenhao Tang Shengxian Cao

School of Automation Engineering,Northeast Electric Power University Jilin,China

国际会议

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)

重庆

英文

1202-1205

2017-10-03(万方平台首次上网日期,不代表论文的发表时间)