Soft Sensing Based on Kernel Isomap

Abstract:

A novel method of soft sensing is propsed combined Kernel Isomap (KIsomap) with Least squares support vector machines (LS-SVM). KIsomap is an improved Isomap and has a generalization property by utilizing kernel trick. It is a kind of novelly promoted nonlinear methods for dimension reduction, and can effectively find out the intrinsic low dimensional structure from high dimensional data. The KIsomap is used to feature extraction and reduce dimensions of sample. The LSSVM is applied to proceed regression modelling, which can not only reduce the complexity of modeling but also improve the generalization ability.The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that KIsomap-LSSVM approach is effective and correct.

• Periodical: Applied Mechanics and Materials (Volumes 278-280)
• Pages: 1349-1352
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.278-280.1349
• Online since: January 2013
• Authors: Qiang Wang, Xue Min Tian
• Keywords: Kernel Isomap, Least Squares Support Vector Machines (Ls-SVM), Soft-Sensing

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