Abstract
In this paper we propose a hybrid model which includes both first principles differential equations and a least squares support vector machine (LS-SVM). It is used to forecast and control an environmental process. This inclusion of the first principles knowledge in this hybrid model is shown to improve substantially the stability of the model predictions in spite of the unmeasurability of some of the key parameters. Proposed hybrid model is compared with both a hybrid neural network(HNN) as well as hybrid neural network with extended kalman filter(HNN-EKF). From experimental results, proposed hybrid model shown to be far superior when used for extrapolation compared to HNN and HNN-EKF.
This study was supported by a grant of the Korea Health 21 R&D Project, Ministry of Health & Welfare, Republic of Korea (02-PJ1-PG6-HI03-0004).
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© 2004 Springer-Verlag Berlin Heidelberg
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Kim, B.J., Kim, I.K. (2004). An Application of Hybrid Least Squares Support Vector Machine to Environmental Process Modeling. In: Liew, KM., Shen, H., See, S., Cai, W., Fan, P., Horiguchi, S. (eds) Parallel and Distributed Computing: Applications and Technologies. PDCAT 2004. Lecture Notes in Computer Science, vol 3320. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30501-9_42
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DOI: https://doi.org/10.1007/978-3-540-30501-9_42
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24013-6
Online ISBN: 978-3-540-30501-9
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