关键词: 非线性时间序列, K-Isomap, 支持向量机, 模式分类, TE过程

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.

Key words: nonlinear time series, K-Isomap, support vector machines, patterns classification, Tennessee Eastman（TE） process