【摘 要】
：
传统MKPLS是对数据矩阵的协方差矩阵进行分解,没有利用数据的高阶统计量等有用信息,在进行特征提取时会造成数据有用信息的丢失,导致故障识别效果差.为了解决此问题,提出了统计量模式分析(Statistics Pattern Analysis, SPA)与多向核偏最小二乘(Multi-way Kernel Partial Least Squares,MKPLS)相结合的多向统计量模式分析的核偏最小二乘
【机 构】
：
北京工业大学电子信息与控制工程学院 北京100124
【出 处】
：
西南大学2014年全国博士生学术论坛(电子技术与信息科学领域)
论文部分内容阅读
传统MKPLS是对数据矩阵的协方差矩阵进行分解,没有利用数据的高阶统计量等有用信息,在进行特征提取时会造成数据有用信息的丢失,导致故障识别效果差.为了解决此问题,提出了统计量模式分析(Statistics Pattern Analysis, SPA)与多向核偏最小二乘(Multi-way Kernel Partial Least Squares,MKPLS)相结合的多向统计量模式分析的核偏最小二乘方法(Multi-way Statistics Pattern Analysis Kernel Partial Least Squares,MSPAKPLS).该方法首先引入滑动窗技术构造样本的不同阶次统计量,将数据从原始的数据空间映射到统计量样本空间,然后利用核函数将统计量样本空间映射到高维核空间进行偏最小二乘分析,并对产品质量进行预测.最后将该方法应用到工业青霉素发酵过程中,并与传统方法进行比较,发现该方法具有更好的监控性能和预测性能.
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