Skip to main content
SpringerLink
Log in

Case Studies

  • Chapter
  • First Online:
Modelling and Control of Dynamic Systems Using Gaussian Process Models

Part of the book series: Advances in Industrial Control ((AIC))

  • 3899 Accesses

Abstract

This chapter describes three examples of the use of GP models. Each example covers selected issues of GP model applications for dynamic systems modelling and control in practice.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 119.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 159.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Likar, B., Kocijan, J.: Predictive control of a gas-liquid separation plant based on a Gaussian process model. Comput. Chem. Eng. 31(3), 142–152 (2007)

    Article  Google Scholar 

  2. Kocijan, J., Likar, B.: Gas-liquid separator modelling and simulation with Gaussian-process models. Simul. Modell. Prac. Theory 16(8), 910–922 (2008)

    Article  Google Scholar 

  3. Vrančić, D., Juričić, D., Petrovčič, J.: Measurements and mathematical modelling of a semi-industrial liquid-gas separator for the purpose of fault diagnosis. Technical Report DP-7260, Jožef Stefan Institute, Ljubljana (1995). http://dsc.ijs.si/Damir.Vrancic/Files/dp7260.pdf

  4. Girard, A.: Approximate methods for propagation of uncertainty with Gaussian process models. Ph.D. thesis, University of Glasgow, Glasgow (2004). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.63.8313

  5. Rasmussen, C.E., Williams, C.K.I.: Gaussian Processes for Machine Learning. MIT Press, Cambridge, MA (2006)

    MATH  Google Scholar 

  6. Kocijan, J., Žunič, G., Strmčnik, S., Vrančić, D.: Fuzzy gain-scheduling control of a gas-liquid separation plant implemented on a PLC. Int. J. Control 75, 1082–1091 (2002)

    Article  MATH  Google Scholar 

  7. Maciejowski, J.M.: Predictive control with constraints. Pearson Education Limited, Harlow (2002)

    Google Scholar 

  8. Kocijan, J., Přikryl, J.: Soft sensor for faulty measurements detection and reconstruction in urban traffic. In: Proceedings 15th IEEE Mediterranean Electromechanical Conference (MELECON), pp. 172–177. Valletta (2010)

    Google Scholar 

  9. Kadlec, P., Gabrys, B., Strand, S.: Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33, 795–814 (2009)

    Article  Google Scholar 

  10. Gonzalez, G.D.: Soft sensors for processing plants. In: Proceedings of the second international conference on intelligent processing and manufacturing of materials, IPMM’99 (1999)

    Google Scholar 

  11. Fortuna, L.: Soft sensors for monitoring and control of industrial processes. Springer, Berlin (2007)

    MATH  Google Scholar 

  12. Adamski, A., Habdank-Wojewodzki, S.: Traffic congestion and incident detector realized by fuzzy discrete dynamic system. Arch. Transp. 17(2), 5–13 (2004)

    Google Scholar 

  13. Zhu, W.B., Li, D.S., Lu, Y.: Real time speed measure while automobile braking on soft sensing technique. J. Phys.: Conf. Ser. 48(1), 730–733 (2006)

    MathSciNet  Google Scholar 

  14. Gupta, M., Gao, J., Aggarwal, C.C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250–2267 (2014)

    Article  Google Scholar 

  15. Hodge, V., Austin, J.: A survey of outlier detection methodologies. Artif. Intell. Rev. 22(2), 85–126 (2004)

    Article  MATH  Google Scholar 

  16. Lin, B., Recke, B., Knudsen, J., Jørgensen, S.B.: A systematic approach for soft sensor development. Comput. Chem. Eng. 31(5), 419–425 (2007)

    Article  Google Scholar 

  17. Menold, P.H., Pearson, R.K., Allgöwer, F.: Online outlier detection and removal. In: Proceedings of the 7th Mediterranean on control and automation (MED’99), pp. 1110–1133. Haifa (1999)

    Google Scholar 

  18. Lee, C., Choi, S., Lee, I.B.: Sensor fault identification based on time-lagged PCA in dynamic processes. Chemom. Intell. Lab. Syst. 70(2), 165–178 (2004)

    Article  Google Scholar 

  19. Osborne, M.A., Garnett, R., Swersky, K., de Freitas, N.: Prediction and fault detection of environmental signals with uncharacterised faults. In: 26th AAAI Conference on Artificial Intelligence (AAAI-12). Toronto (2012)

    Google Scholar 

  20. Gao, Y., Li, Y.: Improving Gaussian process classification with outlier detection, with applications in image classification. In: Kimmel, R.K.R., Sugimoto, A. (eds.) Computer Vision—ACCV 2010, Part IV, LNCS, vol. 6495, pp. 153–164. Springer, Berlin (2011)

    Chapter  Google Scholar 

  21. Smith, M., Reece, S., Roberts, S., Rezek, I.: Online maritime abnormality detection using Gaussian processes and extreme value theory. In: IEEE 12th International Conference on Data Mining (ICDM), pp. 645–654. IEEE (2012)

    Google Scholar 

  22. Google maps. http://maps.google.com/

  23. Shumway, R.H., Stoffer, D.S.: Time Series Analysis and Its Applications, With R Examples. Springer Texts in Statistics, 3rd edn. Springer, New York, NY (2011)

    Book  MATH  Google Scholar 

  24. Noh, H.Y., Rajagopal, R.: Data-driven forecasting algorithms for building energy consumption. In: Lynch, J.P., Yun, C.B., Wang, K.W. (eds.) Proceedings SPIE 8692, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2013, vol. 8692. San Diego, CA (2013). doi:10.1117/12.2009894

    Google Scholar 

  25. Lourenco, J., Santos, P.: Short-term load forecasting using a Gaussian process model: the influence of a derivative term in the input regressor. Intell. Decis. Technol. 6(4), 273–281 (2012)

    Google Scholar 

  26. Dong, D.: Mine gas emission prediction based on Gaussian process model. Procedia Eng. 45(1), 334–338 (2012)

    Article  Google Scholar 

  27. Liu, Z., Xu, W., Shao, J.: Gauss process based approach for application on landslide displacement analysis and prediction. CMES—Comput. Model. Eng. Sci. 84(2), 99–122 (2012)

    Google Scholar 

  28. Ou, P., Wang, H.: Modeling and forecasting stock market volatility by Gaussian processes based on GARCH, EGARCH and GJR models. In: Proceedings of the World Congress on Engineering 2011, WCE 2011, vol. 1, pp. 338–342 (2011)

    Google Scholar 

  29. Peltola, V., Honkela, A.: Variational inference and learning for non-linear state-space models with state-dependent observation noise. In: Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010, pp. 190–195 (2010)

    Google Scholar 

  30. McHutchon, A., Rasmussen, C.E.: Gaussian process training with input noise. In: Shawe-Taylor, J., Zemel, R., Bartlett, P., Pereira, F., Weinberger, K. (eds.) Advances in Neural Information Processing Systems, vol. 24, pp. 1341–1349 (2011)

    Google Scholar 

  31. Petelin, D., Grancharova, A., Kocijan, J.: Evolving Gaussian process models for the prediction of ozone concentration in the air. Simul. Modell. Prac. Theory 33(1), 68–80 (2013)

    Article  Google Scholar 

  32. Hites, R.A.: Elements of environmental chemistry. Wiley - Interscience, Hoboken, NJ (2007)

    Book  Google Scholar 

  33. Horvath, M., Bilitzky, L., Huttner, J.: Ozone. Elsevier, Amsterdam (1985)

    Google Scholar 

  34. Bytnerowicz, A., Omasa, K., Paoletti, E.: Integrated effects of air pollution and climate change on forests: a northern hemisphere perspective. Environ. Pollut. 147(3), 438–445 (2006)

    Article  Google Scholar 

  35. Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008 on ambient air quality and cleaner air for Europe. http://ec.europa.eu/environment/air/legis.htm

  36. Al-Alawi, S.M., Abdul-Wahab, S.A., Bakheit, C.S.: Combining principal component regression and artificial neural-networks for more accurate predictions of ground-level ozone. Environ. Modell. Softw. 23, 396–403 (2008)

    Article  Google Scholar 

  37. Grašič, B., Mlakar, P., Božnar, M.: Ozone prediction based on neural networks and Gaussian processes. Nuovo cimento Soc. ital. fis., C Geophys. space phys. 29(6), 651–661 (2006)

    Google Scholar 

  38. Solaiman, T.A., Coulibaly, P., Kanaroglou, P.: Ground-level ozone forecasting using data-driven methods. Air Qual. Atmos. Health 1, 179–193 (2008)

    Article  Google Scholar 

  39. Pisoni, E., Farina, M., Carnevale, C., Piroddi, L.: Forecasting peak air pollution levels using NARX models. Eng. Appl. Artif. Intell. 22, 593–602 (2009)

    Article  Google Scholar 

  40. Lin, Y., Cobourn, W.G.: Fuzzy system models combined with nonlinear regression for daily ground-level ozone predictions. Atmos. Environ. 41, 3502–3513 (2007)

    Article  Google Scholar 

  41. Nebot, A., Mugica, V., Escobet, A.: Ozone prediction based on meteorological variables: a fuzzy inductive reasoning approach. Atmos. Chem. Phys. Discuss. 8, 12343–12370 (2008)

    Article  Google Scholar 

  42. Chelani, A.B.: Prediction of daily maximum ground ozone concentration using support vector machine. Environ. Monit. Assess. 162, 169–176 (2010)

    Article  Google Scholar 

  43. Dueñas, C., Fernández, M.C., Cañete, S., Carretero, J., Liger, E.: Stochastic model to forecast ground-level ozone concentration at urban and rural areas. Chemosphere 61, 1379–1389 (2005)

    Article  Google Scholar 

  44. Grancharova, A., Kocijan, J., Krastev, A., Hristova, H.: High order Gaussian process models for prediction of ozone concentration in the air. In: Proceedings of the 7th EUROSIM Congress on Modelling and Simulation, EUROSIM’10 (2010)

    Google Scholar 

  45. Grancharova, A., Nedialkov, D., Kocijan, J., Hristova, H., Krastev, A.: Application of Gaussian processes to the prediction of ozone concentration in the air of Burgas. In: Proceedings of International Conference on Automatics and Informatics, pp. IV-17-IV-20 (2009)

    Google Scholar 

  46. Feng, Y., Zhang, W., Sun, D., Zhang, L.: Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification. Atmos. Environ. 45, 1979–1985 (2011)

    Article  Google Scholar 

  47. Csató, L., Opper, M.: Sparse online Gaussian processes. Neural Comput. 14(3), 641–668 (2002)

    Article  MATH  Google Scholar 

  48. Petelin, D., Kocijan, J., Grancharova, A.: On-line Gaussian process model for the prediction of the ozone concentration in the air. Comptes Rendus de L’Academie Bulgare des Sciences 64(2013), 117–124 (2011)

    Google Scholar 

  49. Seeger, M., Williams, C.K.I., Lawrence, N.D.: Fast forward selection to speed up sparse Gaussian process regression. In: Ninth International Workshop on Artificial Intelligence and Statistics, Society for Artificial Intelligence and Statistics (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Systems and Control, Jožef Stefan Institute, Ljubljana, Slovenia

    Juš Kocijan

  2. Centre for Systems and Information Technologies, University of Nova Gorica, Nova Gorica, Slovenia

    Juš Kocijan

Authors
  1. Juš Kocijan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Juš Kocijan .

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Kocijan, J. (2016). Case Studies. In: Modelling and Control of Dynamic Systems Using Gaussian Process Models. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-21021-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-21021-6_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21020-9

  • Online ISBN: 978-3-319-21021-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation