Integrated Structure and Parameter Selection for Eng-genes Neural Models

  • Patrick Connally
  • Kang Li
  • George W. Irwin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)


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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bohlin, T.: Case study of grey box identification. Automatica 30(2), 307–318 (1994)CrossRefzbMATHGoogle Scholar
  2. 2.
    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)CrossRefGoogle Scholar
  3. 3.
    Li, K.: Eng-genes: A new genetic modelling approach for nonlinear dynamic systems. In: Proceedings of the 16th IFAC World Congress (2005)Google Scholar
  4. 4.
    Funahashi, K.I.: On the approximate realization of continuous mappings by neural networks. Neural Networks 2, 183–192 (1989)CrossRefGoogle Scholar
  5. 5.
    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)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Psichogios, D.C., Ungar, L.H.: A hybrid neural network - first principles approach to process modeling. AiChE 38, 1499–1511 (1992)CrossRefGoogle Scholar
  7. 7.
    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)CrossRefMathSciNetzbMATHGoogle Scholar
  8. 8.
    Hagan, M.H., Menhaj, M.B.: Training feedforward networks with the marquardt algorithm. IEEE Transactions on Neural Networks 5(6), 989–993 (1994)CrossRefGoogle Scholar
  9. 9.
    Connally, P., Li, K., Irwin, G.W.: Two applications of eng-genes bases nonlinear identification. In: Proceedings of the 16th IFAC World Congress (2005)Google Scholar
  10. 10.
    Li, K., Peng, J.X., Irwin, G.W.: A fast nonlinear model identification method. IEEE Transactions on Automatic Control 50(8), 1211–1216 (2005)CrossRefMathSciNetGoogle Scholar
  11. 11.
    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)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Patrick Connally
    • 1
  • Kang Li
    • 1
  • George W. Irwin
    • 1
  1. 1.Intelligent Systems and Control Research GroupQueen’s University BelfastBelfastUK

Personalised recommendations