Using analytic QP and sparseness to speed training of support vector machines
Proceedings of the 1998 conference on Advances in neural information processing systems II
Efficient SVM Regression Training with SMO
Machine Learning
XML Data Management: Native XML and XML Enabled DataBase Systems
XML Data Management: Native XML and XML Enabled DataBase Systems
Indexing and Querying XML Data for Regular Path Expressions
Proceedings of the 27th International Conference on Very Large Data Bases
Accurate on-line support vector regression
Neural Computation
Improvements to the SMO algorithm for SVM regression
IEEE Transactions on Neural Networks
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In the scheme of boiler combustion optimization, a group of optimal controller settings is found to provide recommendations to balance desired thermal efficiency and lowest emissions limit. Characteristic functions between particular objectives and controlling variables can be approximated based on data sets obtained from field tests. These relationships can change with variations in coal quality, slag/soot deposits and the condition of plant equipment, which can not be sampled on-line. Thus, approximation relationships based on test conditions could have little applicability for on-line optimization of the combustion process. In this paper, a new approach is proposed to adaptively perform function approximation based on a modified accurate on-line support vector regression method. Two modified criteria are proposed for selection of the unwanted trained sample to be removed. A structural matrix is used to process and save the model parameters and training data sets, which can be adaptively regulated by the on-line learning method. The proposed method is illustrated with an example and is also applied to real boiler data successfully. The results reveal their validity in the prediction of NO"x emissions and function approximation, which can correctly be adapted to actual variable operating conditions in the boiler.