Abstract
A new approach to the construction and optimisation of ‘eng-genes’ grey-box neural networks is investigated. A forward selection algorithm is used to optimise both the network weights and biases and the parameters of the system-derived activation functions. The algorithm is used for both conventional neural network and eng-genes modelling of a simulated Continuously Stirred Tank Reactor. The resulting eng-genes networks demonstrate superior simulation performance and transparency over a range of network sizes.
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References
Bohlin, T.: Case study of grey box identification. Automatica 30(2), 307–318 (1994)
Li, K., Thompson, S., Peng, J.: Modelling and prediction of nox emission in a coal-fired power generation plant. Control Engineering Practice 12, 707–723 (2004)
Li, K.: Eng-genes: A new genetic modelling approach for nonlinear dynamic systems. In: Proceedings of the 16th IFAC World Congress (2005)
Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2, 183–192 (1989)
Kolmogorov, A.N.: On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition. Dokl. Akad. Nauk USSR 114, 953–956 (1957)
Psichogios, D.C., Ungar, L.H.: A hybrid neural network - first principles approach to process modeling. AiChE 38, 1499–1511 (1992)
Chen, S., Billings, S.A., Luo, W.: Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control 50(5), 1873–1896 (1989)
Hagan, M.H., Menhaj, M.B.: Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)
Connally, P., Li, K., Irwin, G.W.: Two applications of eng-genes bases nonlinear identification. In: Proceedings of the 16th IFAC World Congress (2005)
Li, K., Peng, J.X., Irwin, G.W.: A fast nonlinear model identification method. IEEE Transactions on Automatic Control 50(8), 1211–1216 (2005)
Morningred, J.D., Paden, B.E., Seborg, D.E., Mellichamp, D.A.: An adaptive nonlinear predictive controller. In: Proc. American Control Conference, vol. 2, pp. 1614–1619 (1990)
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© 2006 Springer-Verlag Berlin Heidelberg
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Connally, P., Li, K., Irwin, G.W. (2006). Integrated Structure and Parameter Selection for Eng-genes Neural Models. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Computing. ICIC 2006. Lecture Notes in Computer Science, vol 4113. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11816157_17
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DOI: https://doi.org/10.1007/11816157_17
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37271-4
Online ISBN: 978-3-540-37273-8
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