Soft Sensing Based on EMD and Improved PSO-SVM

Abstract:

A kind of soft sensing is proposed by combining empirical mode decomposition(EMD) with support vector machine optimized by improved particle swarm optimization (IPSO-SVM). EMD is a highly adaptive decomposition and can decompose any complicated signal into so called Intrinsic Mode Functions (IMF), which not only has excellent performance of feature extraction but also can reduce the dimension of the model input data space. we can extracts IMF energy feature as the input feature vectors of IPSO-SVM. Support vector machine (SVM) has been successfully employed to solve regression problem but it is difficult to select appropriate SVM parameters. A new SVM model based on adaptive particle swarm optimization (APSO) for parameter optimization is proposed which not only has strong global search capability, but also is very easy to implement. The proposed method is used to build soft sensing of diesel oil solidifying point. Compared with other two models, the result shows that IPSO-SVM approach has a better prediction and generalization.

• Periodical: Applied Mechanics and Materials (Volumes 291-294)
• Pages: 2817-2821
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.291-294.2817
• Online since: February 2013
• Authors: Qiang Wang, Xue Min Tian
• Keywords: Adaptive Particle Swarm Optimization (APSO), Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Soft-Sensing

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