OPTIMIZED MODELING OF WOOD DRYING PROCESS USING KERNAL PRINCIPAL COMPONENT ANALYSIS AND PSO-SVM

摘要：

It is the key to establish the model which can describe wood drying regular accurately and integrally to realize online predictive control and improve control level in wood drying process.To aim at the test sample data that had much interference and redundancy in drying process, this paper adopted Kernel Principal Component Analysis (KPCA) to pretreat wood drying data, and then established the wood drying model based on PSO-SVM.The results of simulation experiments showed that the model established on the basis of the pretreated and dimension reduced training sample data had strong practicability, and could get better predictive accuracy, less computations and higher computing speed.

关键词： KPCA(Kernel Principal Component Analysis) SVM(Support Vector Machines) PSO(Particle Swarm Optimization) wood drying modeling

作者: Dong Zhang Chunyan Zhang Liping Sun Jun Cao

作者单位: College of Electromechanics Engineering,Northeast Forestry University26 Hexing Road,Xiangfang Distri Department of Electromechanics Engineering,Huaxia Professional Institute of Computer Technology9 Xue College of Electromechanics Engineering,Northeast Forestry University 26 Hexing Road,Xiangfang Distr

会议类型: 国际会议

会议名称: The 7th Asia-Pacific Drying Conference(第七届亚太地区干燥会议 ADC2011)

会议地点: 天津

会议语种:英文

页码: 1-5

在线出版日期: 2011-09-18（万方平台首次上网日期，不代表论文的发表时间）