摘要
针对锌钡白煅烧过程建模难的问题 ,采用一种基于最小二乘支持向量机 (LS SVM )的辨识算法进行过程建模研究 .从SVM与LS SVM的算法机理出发 ,利用LS SVM算法结构简单、辨识速度快的优点 ,通过建模仿真得到煅烧转速随煅烧温度变化的模型 ,并将此算法与自适应神经模糊推理系统 (ANFIS)进行了辨识性能上的对比 ,结果表明LS SVM在过程建模中具有更好的实际应用价值 .
In order to overcome the difficulty in the modeling of lithopone calcination, an identification algorithm based on the LS-SVM (Least Squares Support Vector Machine) was applied to the modeling of the calcination process. As the LS-SVM algorithm is of the advantages of simple structure and high speed, according to the mechanisms of SVM and LS-SVM algorithms, a model describing the variation of rotating speed with the temperature in the calcinations process was obtained by modeling simulation. The identification performance of the proposed algorithm was finally compared with that of the ANFIS (Adaptive Neural-fuzzy Inference System), with the conclusion that the LS-SVM is more valuable when applied to the modeling of the calcination process.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2004年第11期46-50,共5页
Journal of South China University of Technology(Natural Science Edition)
基金
广东省科技厅工业攻关资助项目 (C10 90 9)
广州市科技局工业攻关资助项目 (2 0 03Z3-D0 0 91)
关键词
锌钡白
煅烧
最小二乘支持向量机
建模
lithopone
calcination
least squares support vector machine
modeling
