Abstract
Soft sensors have been widely used in industrial process control to improve the quality of product and assure safety in production. This paper introduces support vector machines (SVM) into soft-sensing modeling. Building the models, on one hand we want to have the best set of input variables, on the other hand we want to get the best possible performance of the SVM model. So the Genetic Algorithms is used to choose the input variables and select the parameters of SVM. Moreover, training the model on data coming a real experiment process—Nosiheptide fermentation process and evaluating the model performance on the same process. Results show that SVM model optimized by Genetic Algorithms provides a new and effective method for soft- sensing modeling and has promising application in industrial process applications.
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Haifeng, S., Weiqi, Y., Fuli, W., Dakuo, H. (2007). Support Vector Machines and Genetic Algorithms for Soft-Sensing Modeling. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_42
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DOI: https://doi.org/10.1007/978-3-540-72395-0_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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