Online Soft Sensing Method Based on Selective Kernel Learning
ZHAO Chengbin
CHEN Peng
LIANG Jie
Abstract：The performance of off-line build soft sensor often deteriorates,where updating the model online is necessary. For this reason,an approach based on selective kernel learning for on line soft sensing is proposed. This method,utilizing the least squares support vector machine(LSSVM)for constructing offline model,employs the strategy of prediction error bound(PEB)to carry out forward learning selectively so as to enhance model sparsity. Moreover,in order to delete redundant samples more accurate?ly in backward learning,this paper proposes a similarity criterion in high dimensional feature space which incorporates the input and output information simultaneously so that the most dissimilar sample to the current state is selected and eliminated. The forego?ing scheme is applied to build the soft sensor of melt index of polypropylene and the result has demonstrated the effectiveness of the proposed method.
Keywords：soft sensingselective kernel learningleast squares support vector machinesimilarity
Publication Date：2018-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 26-29,57 )