A LLE-Based HMM Applied to the Prediction of Kiln Coal Feeding Trend

摘要：

　　As the data collected in rotary kiln is rather nonlinear,linear transforming such as PCA、ICA and LPP to extract feature is not ideal,while manifold learning performs well in high dimensional nonlinear data transform.A new Hidden Markov Model (HMM) based method combined with Locally Linear Embedding (LLE) to predict the coal feeding trend is put forward.Firstly,LLE-HMM conducts nonlinear feature transforms on the sample data by LLE,then the feature data is quantized into observation symbol and HMM is establish to predict the coal feeding trend finally.Through the simulation of the sample data in rotary kiln production process and compared with PCA-HMM、ICA-HMM、LPP-HMM,the results of LLE-HMM shows that it has higher measurement accuracy,better tracking performance,which can satisfy the prediction of coal feeding requirements.

关键词： Manifold Learning LLE HMM Coal Feeding ICA LPP

作者: Yunlong Liu Zhang Xiaogang

作者单位: College of Electrical and Information Engineering,Hunan University,Changsha 410082

会议类型: 国际会议

会议名称: Chinese Conference on Pattern Recognition, CCPR(2014年全国模式识别学术会议)

会议地点: 长沙

会议语种:英文

页码: 111-119

在线出版日期: 2014-11-01（万方平台首次上网日期，不代表论文的发表时间）