Direct Blowing Pulverizing System Modeling Based on IPLS-SVM
ICICTA '14 Proceedings of the 2014 7th International Conference on Intelligent Computation Technology and Automation（2014）
摘要
In order to solve the problem that the output of ball mill pulverizing system is difficult to directly measured in thermal power plant with double inlet and double outlet ball mill pulverizing system which is a large delay, strong nonlinear system. It introduces the pruning method to improve the incremental least square support vector machine's sparsity that based on the incremental least square support vector machine algorithm. The LS-SVM model is simplifies by not only deleting some too big or too small training samples at the same time, but also deleting the large change rate data. It avoids the influence of bad samples on model, and simplifies the LS-SVM model. Incremental pruning least squares support vector machine algorithm (IPLS-SVM) is used for the soft measurement model of direct blowing pulverizing steel ball coal mill with double inlet and double outlet. Compared the modified model for simulation, simulation results show that the speed of convergence is faster, more suitable for online learning.
更多查看译文
关键词
direct blowing pulverizing system,direct blowing pulverizing steel ball coal mill,steel ball coal mill,direct blowing pulverizing system modeling,thermal power stations,learning (artificial intelligence),ls-svm model,steel,large-change rate data deletion,online learning,ball milling,ipls-svm,double-inlet-and-double-outlet ball mill pulverizing system,convergence,pruning method,soft measurement model,incremental least square support vector machine algorithm,soft sensor modeling,pulverised fuels,coal,thermal power plant,mechanical engineering computing,incremental pruning least squares support vector machine algorithm,incremental least square support vector machine sparsity improvement,support vector machines,prediction algorithms,predictive models,mathematical model
AI 理解论文
溯源树
样例
生成溯源树，研究论文发展脉络
Chat Paper
正在生成论文摘要