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Staged Neural Modeling with Application to Prediction of NOx Pollutant Concentrations in Urban Air

  • Kang Li
  • Barbara Pizzileo
  • Adetutu Ogle
  • Colm Scott
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4113)

Abstract

Addressing the drawbacks of widely used forward neural network growing methods in neural modeling of time series and nonlinear dynamic systems, a staged algorithm is proposed in this paper for modeling and prediction of NOx Pollutant Concentrations in urban air in Belfast, Northern Ireland, using generalized single-layer network. In this algorithm, forward method is used for neural network growing, the resultant network is then refined at the second stage to remove inefficient hidden nodes. Application study confirms the effectiveness of the proposed method.

Keywords

Nitric Oxide Neural Modeling Neural Network Prediction Nonlinear System Identification Candidate Pool 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    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
  2. 2.
    Shi, J.P., Harrison, R.M.: Regression Modeling of Hourly NOx and NO2 Concentrations in Urban Air in London. Atmospheric Envrionment 31, 4081–4094 (1997)CrossRefGoogle Scholar
  3. 3.
    Igelnik, B., Pao, Y.H.: Additional Perspectives of Feedforward Neural-nets and the Functional-link. In: IJCNN 1993, Nagoya, Japan, pp. 2284–2287 (1993)Google Scholar
  4. 4.
    Adeney, K.M., Korenberg, M.J.: Iterative Fast Orthogonal Search Algorithm for MDL-based Training of Generalized Single-layer Networks. Neural Networks 13, 787–799 (2000)CrossRefGoogle Scholar
  5. 5.
    Nelles, O.: Nonlinear System Identification. Springer, Heidelberg (2001)MATHGoogle Scholar
  6. 6.
    Chen, S., Billings, S.A., Luo, W.: Orthogonal Least Squares Methods and Their Application to Non-linear System Identification. Int. J. Control 50, 1873–1896 (1989)MATHCrossRefMathSciNetGoogle Scholar
  7. 7.
    Li, K., Peng, J., Irwin, G.: A Fast Nonlinear Model Identification Method. IEEE Transactions on Automatic Control 50, 1211–1216 (2005)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Akaike, H.: A New Look at the Statistical Model Identification. J. R. Statist. Soc. Ser. B 36, 117–147 (1974)Google Scholar
  9. 9.
    Sherstinsky, A., Picard, R.W.: On the Efficiency of the Orthogonal Least Squares Training Method for Radial Basis Function Networks. IEEE Trans. on Neural Networks 7, 195–200 (1996)CrossRefGoogle Scholar
  10. 10.
    Liu, G.P., Kadirkamanathan, V., Billings, S.A.: On-line Identification of Nonlinear Systems Using Volterra Polynomial Basis Function Neural Networks. Neural Networks 11, 1645–1657 (1998)CrossRefGoogle Scholar
  11. 11.
    Department for Environment, Food and Rural Affairs: Estimated Emissions of Nitrogen Oxides (NOX) by UNECE Source Category, Type of Fuel and End User for Large Combustion Plants (LCPs) 1970–2004. e-Digest of Environmental Statistics, Published (March 2006) Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kang Li
    • 1
  • Barbara Pizzileo
    • 1
  • Adetutu Ogle
    • 2
  • Colm Scott
    • 1
  1. 1.School of Electronics, Electronic Engineering & Computer ScienceQueen’s University BelfastBelfastU.K.
  2. 2.ATU, QUESTOR centreQueen’s University BelfastBelfastUK

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