ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements

Kamil Dedecius, Pavel Ettler

2011

Abstract

Industrial model-based control often relies on parametric models. However, for certain operational conditions either the precise underlying physical model is not available or the lack of relevant or reliable data prevents its use. A popular approach is to employ the black box or grey box models, releasing the theoretical rigor. This leads to several candidate models being at disposal, from which the (often subjectively) prominent one is selected. However, in the presence of model uncertainty, we propose to benefit from a subset of credible models. The idea behind the multimodelling approach is closely related to hierarchical modelling methodology. By using several modelling levels, it is possible to achieve relatively high quality and robust solution, providing a way around typical constraints in industrial applications.

References

  1. Bernardo, J. and Smith, A. (2001). Bayesian Theory. Measurement Science and Technology, 12:221.
  2. Bernardo, J. and Smith, A. (2001). Bayesian Theory. Measurement Science and Technology, 12:221.
  3. Bohlin, T. (1991). Interactive System Identification: Prospects and Pitfalls. Springer-Verlag, Berlin, Heidelberg, New York.
  4. Bohlin, T. (1991). Interactive System Identification: Prospects and Pitfalls. Springer-Verlag, Berlin, Heidelberg, New York.
  5. Dedecius, K. (2010). Partial Forgetting in Bayesian Estimation. PhD thesis, Czech Technical University in Prague.
  6. Dedecius, K. (2010). Partial Forgetting in Bayesian Estimation. PhD thesis, Czech Technical University in Prague.
  7. Karush, J. (1961). On the Chapman-Kolmogorov Equation. The Annals of Mathematical Statistics, 32(4):1333- 1337.
  8. Karush, J. (1961). On the Chapman-Kolmogorov Equation. The Annals of Mathematical Statistics, 32(4):1333- 1337.
  9. KulhavÉ, R. and KárnÉ, M. (1984). Tracking of Slowly Varying Parameters by Directional Forgetting. In 9th IFAC World Congress, Budapest, Hungary.
  10. KulhavÉ, R. and KárnÉ, M. (1984). Tracking of Slowly Varying Parameters by Directional Forgetting. In 9th IFAC World Congress, Budapest, Hungary.
  11. Murphy, K. (2007). Conjugate Bayesian Analysis of the Gaussian Distribution.
  12. Murphy, K. (2007). Conjugate Bayesian Analysis of the Gaussian Distribution.
  13. Peterka, V. (1981). Bayesian Approach to System Identification In P. Eykhoff (Ed.) Trends and Progress in System Identification.
  14. Peterka, V. (1981). Bayesian Approach to System Identification In P. Eykhoff (Ed.) Trends and Progress in System Identification.
  15. Raftery, A., KárnÉ, M., and Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics: a journal of statistics for the physical, chemical, and engineering sciences, 52(1):52.
  16. Raftery, A., KárnÉ, M., and Ettler, P. (2010). Online Prediction Under Model Uncertainty via Dynamic Model Averaging: Application to a Cold Rolling Mill. Technometrics: a journal of statistics for the physical, chemical, and engineering sciences, 52(1):52.
Download


Paper Citation


in Harvard Style

Dedecius K. and Ettler P. (2011). ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-74-4, pages 284-289. DOI: 10.5220/0003533402840289


in Harvard Style

Dedecius K. and Ettler P. (2011). ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements . In Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-8425-74-4, pages 284-289. DOI: 10.5220/0003533402840289


in Bibtex Style

@conference{icinco11,
author={Kamil Dedecius and Pavel Ettler},
title={ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2011},
pages={284-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003533402840289},
isbn={978-989-8425-74-4},
}


in Bibtex Style

@conference{icinco11,
author={Kamil Dedecius and Pavel Ettler},
title={ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements},
booktitle={Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2011},
pages={284-289},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003533402840289},
isbn={978-989-8425-74-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements
SN - 978-989-8425-74-4
AU - Dedecius K.
AU - Ettler P.
PY - 2011
SP - 284
EP - 289
DO - 10.5220/0003533402840289


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - ADAPTIVE CONTINUOUS HIERARCHICAL MODEL-BASED DECISION MAKING - For Process Modelling with Realistic Requirements
SN - 978-989-8425-74-4
AU - Dedecius K.
AU - Ettler P.
PY - 2011
SP - 284
EP - 289
DO - 10.5220/0003533402840289