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 (A05-0909-A80405-05N1-00000A).
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References
Joseph, B., Brosilou, C.: Inferential Control of Processes, Part I Steady State Analysis and Design. AICHE J. 24(3) (1978)
Lindskog, P., Ljung, L.: Ensuring Certain Physical Properties in Black Box Model by Applying Fuzzy Techniques, Technical Report (1996)
Brosilow, C., Tong, M.: Inference Control of Processes, Part II The Structure and Dynamics of Inferential Control Systems. AIChE J. 24(3) (1978)
Acceessible at http://www.ici.ro/ici/revista/sic99-1/art04.html
Molga, E.J., Westerterp, K.R.: Neural network based model of the kinetics of catalytic hydrogeneration reactions. Stud. Surf. Sci. Catal. 109, 379–388 (1997)
Tesen, A.Y.D., et al.: Predictive control of quality in batch polymerization using hybrid ANN models. AICHE Journal 42(2), 455–465 (1996)
Saxen, B., Saxen, H.: A neural network based model of bioreaction kinetics. Canadian Journal of Chemical Engineering 74(1), 124–131 (1996)
Weich, G., Bishop, G.: An Introduction to the Kalman
Dimitris, C., Psichogios,, Ungar, H.: A Hybrid Neural Network-First Principles Approach to Process Modeling. AICHE Journal 38(10), 14991511 (1992)
Lant, P.A., Williams, M.J., Montague, G.A., Tham, M.T., Morris, A.J.: A Comparision of Adaptive Estimation With Neural Based Techniques for Bioprocess Application. In: Proc. of the American Control Conf. 2713 (1990)
Gunn, S.: Support Vector Machines for Classification and Regression, SIS Technical Report, U. of Southampton (1998)
Suykens, J.A.K.: Nonlinear Modeling and Support Vector Machines, Accessible at, http://www.kdiss.or.kr/kdiss/
Shao, X.: Model Selection Using Statistical Learning Theory, Ph. D. Thesis, U. of Minnesota (1999)
Accessible at http://www.ics.uci.edu/~mlearn/MLRepository.html
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© 2006 Springer-Verlag Berlin Heidelberg
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Kim, B.J., Kim, I.K. (2006). A Novel Hybrid System for Dynamic Control. In: Wang, TD., et al. Simulated Evolution and Learning. SEAL 2006. Lecture Notes in Computer Science, vol 4247. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11903697_77
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DOI: https://doi.org/10.1007/11903697_77
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-47331-2
Online ISBN: 978-3-540-47332-9
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