计算机工程与应用2011，Vol.47Issue(32)：128-131,4.DOI:10.3778/j.issn.1002-8331.2011.32.037
非线性多维时问序列模式分类的新方法
Novel method for patterns classification of nonlinear multidimensional time series
摘要
Abstract
Pattern classification from nonlinear multivariate time series is an important problem in process engineering.This paper introduces a generic approach to detect patterns and identify their class incorporating manifold learning and support vector classifier.K-Isomap.a kernelized manifold learning algorithm,is employed to project multidimensional nonlinear time series onto low-dimensional feature space and realize nonlinear dimensionality reduction.Pattern classifier is designed to identify the pattern of nonlinear time series based on support vector machines in low-dimensional feature space.This method takes the advantage of the kernelized manifold learning algorithm and obtains better performance.Experimental results on Tennessee Eastman(TE) process demonstrate the validity and effectiveness of the proposed method.关键词
非线性时间序列/K-Isomap/支持向量机/模式分类/TE过程Key words
nonlinear time series/K-Isomap/support vector machines/patterns classification/Tennessee Eastman(TE) process分类
信息技术与安全科学引用本文复制引用
程健,陈光昀,龚平华,朱小强..非线性多维时问序列模式分类的新方法[J].计算机工程与应用,2011,47(32):128-131,4.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.60835002) （the National Natural Science Foundation of China under Grant No.60835002）
国家博士后科学基金(No.20090460328). （No.20090460328）
