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Alternative TitleSimulation of MICA Based on a New Type of Data Prediction
白丽娜; 高翔; 苑明哲; 曹景兴
Source Publication计算机仿真
Indexed ByCSCD
Contribution Rank1
Funding Organization国家高技术研究发展计划(863计划)(2006AA04Z185);;辽宁省教育厅项目(2005320)
Keyword广义相关系数法 间歇过程 多向独立元分析法 故障诊断
Other AbstractIn Multivariate Statistical process monitor,an approach of combining Multiway Independent Component Analysis with Generalized Correlation Coefficients (GCC) is presented to deal with the unknown statistic distribution of the data for overcoming the phenomenon of improper diagnosis and to carry out the simulation of online monitoring process. MICA could separate the correlation of variables without considering whether the model data follow the normal distribution or not,and the independent component variables are able to describe the variation of the process well. Furthermore,to improve the ability of predicting future process fault,GCC method is also presented for predicting unknown future data to ascertain the proper corresponding multi-trajectories in history model library and copy the part from the current time point as supplement for the running trajectories being tested. The data from polymerization process of polyvinyl chloride are sampled for simulation of online process monitoring,and the results show that the new prediction method is more effective than other traditional ones for online process monitoring and fault diagnosis.
Citation statistics
Document Type期刊论文
Corresponding Author白丽娜
Recommended Citation
GB/T 7714
白丽娜,高翔,苑明哲,等. 一种基于新型数据预测方法的MICA仿真研究[J]. 计算机仿真,2009,26(2):134-138.
APA 白丽娜,高翔,苑明哲,&曹景兴.(2009).一种基于新型数据预测方法的MICA仿真研究.计算机仿真,26(2),134-138.
MLA 白丽娜,et al."一种基于新型数据预测方法的MICA仿真研究".计算机仿真 26.2(2009):134-138.
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