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The Use of Experimental Data in Simulation Model Validation

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Computer Simulation Validation

Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

Abstract

The use of experimental data for the validation of deterministic dynamic simulation models based on sets of ordinary differential equations and algebraic equations is discussed. Comparisons of model and target system data are considered using graphical methods and quantitative measures in the time and frequency domains. System identification and parameter estimation methods are emphasized, especially in terms of identifiability analysis which can provide valuable information for experiment design. In general, experiments that are suitable for system identification are also appropriate for model validation. However, there is a dilemma since models are needed for this design process. The experiment design, data collection and analysis of model validation results is, inevitably, an iterative process, and experiments designed for model validation can never be truly optimal. A model of the pulmonary gas exchange processes in humans is used to illustrate some issues of identifiability, experiment design and test input selection for model validation.

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References

  • Bache, R. A. (1981). Time-domain system identification applied to non-invasive estimation of cardio-pulmonary quantities. Ph.D. thesis, University of Glasgow, UK.

    Google Scholar 

  • Bache, R. A., Gray, W. M., & Murray-Smith, D. J. (1981). Time-domain system identification applied to non-invasive estimation of cardio-pulmonary quantities. IEE Proceedings, 128, Part D, 56–64.

    Google Scholar 

  • Bache, R. A., & Murray-Smith, D. J. (1983). Structural and parameter identification of two lung gas-exchange models. In G. C. Vansteenkiste & P. C. Young (Eds.), Modelling and data analysis in biotechnology and medical engineering (pp. 175–188). Amsterdam, The Netherlands: North-Holland.

    Google Scholar 

  • Beck, J. V., & Arnold, K. J. (1977). Parameter estimation in science and engineering. New York, NY: Wiley.

    MATH  Google Scholar 

  • Bellman, R., & Ã…ström, K. J. (1970). On structural identifiability. Mathematical Biosciences, 7, 329–339.

    Article  Google Scholar 

  • Bradley, R., Padfield, G. D., Murray-Smith, D. J., & Thomson, D. G. (1990). Validation of helicopter mathematical models. Transactions of the Institute of Measurement and Control, 12, 186–196.

    Article  Google Scholar 

  • Brown, F., & Godfrey, K. R. (1978). Problems of determinacy in compartmental modeling with application to bilirubin kinetics. Mathematical Biosciences, 40, 205–224.

    Article  Google Scholar 

  • Bryce, G. W., Foord, T. R., Murray-Smith, D. J., & Agnew, P. (1976). Hybrid simulation of water-turbine governors. In R. E. Crosbie & J. L. Hay (Eds.), Simulation councils proceedings series (Vol. 6(1), pp. 35–44). La Jolla CA: Simulation Councils Inc.

    Google Scholar 

  • Chatfield, C. (1996). The analysis of time series. An introduction (5th ed.). London, UK: Chapman and Hall.

    MATH  Google Scholar 

  • Federov, V. V. (1972). Theory of optimal experiments. New York, NY: Academic Press.

    Google Scholar 

  • Goodwin, G. C., & Payne, R. L. (1977). Dynamic system identification: Experiment design and data analysis. New York, NY: Academic Press.

    MATH  Google Scholar 

  • Gustavsson, I. (1972). Comparison of different methods for identification of industrial processes. Automatica, 8, 127–142.

    Article  Google Scholar 

  • Hahn C. E. W. & Farmery, A. D. (2003). Gas exchange modelling: no more gills please. British Journal Anaesthesia, 91(1): 2–15.

    Google Scholar 

  • Heylen, W., & Lammens, S. (1996). FRAC: A consistent way of comparing frequency response functions. In M. I. Friswell & J. E. Mottershead (Eds.), Proceedings of Conference on Identification in Engineering Systems, Swansea, 1996 (pp. 48–57). Swansea, UK: University of Wales.

    Google Scholar 

  • Hughes, I. G., & Hase, T. P. A. (2010). Measurements and their uncertainties: A practical guide to modern error analysis. Oxford, UK: Oxford University Press.

    MATH  Google Scholar 

  • Hunter, W. G., Hill, W. J., & Henson, T. L. (1969). Designing experiments for precise estimation of βsome of the constants in a mechanistic model. Canadian Journal of Chemical Engineering, 47, 76–80.

    Article  Google Scholar 

  • Huynh, D. P. B., Knezevic, D. J., & Patera, A. T. (2012). Certified reduced basis model characterization: a frequentistic uncertainty framework. Computer Methods in Applied Mechanics and Engineering, 201, 13–24.

    Article  MathSciNet  Google Scholar 

  • Jachner, S., van den Boogaart, K. G., & Petzoldt, T. (2007) Statistical methods for the qualitative assessment of dynamic models with time delay (R Package qualV). Journal of Statistical Software, 22(8).

    Google Scholar 

  • Kammel, G., Voigt, H. M., & Neβ, K. (2005). Development of a tool to improve the forecast accuracy of dynamic simulation models for the paper process. In: J. Kappen, J. Manninen, & R. Ritala (Eds.), Proceedings of Model Validation Workshop, 6th October 2005, Espoo, Finland. Espoo, Finland: VTT Technical Research Centre.

    Google Scholar 

  • Kocijan, J., Girard, A., Banko, B., & Murray-Smith, R. (2005). Dynamic system identification with dynamic processes. Mathematical and Computer Modelling of Dynamic Systems, 11, 411–424.

    Article  Google Scholar 

  • Ljung, L. (1999). System identification: Theory for the user (2nd ed.). Upper Saddle River, NJ: Prentice Hall.

    MATH  Google Scholar 

  • McFarland, J., & Mahadevan, S. (2008). Multivariate significance tests and model calibration under uncertainty. Computer Methods in Applied Mechanics and Engineering, 197(29–32), 2407–2479.

    MATH  Google Scholar 

  • Murphy, T. W. (1969). Modelling of lung gas exchange - mathematical models of the lung. Mathematical Biosciences, 5, 427–447.

    Article  Google Scholar 

  • Murray-Smith, D. J. (2012). Modelling and simulation of integrated systems in engineering: Issues of methodology, quality, testing and application. Cambridge, UK: Woodhead Publishing.

    Book  Google Scholar 

  • Murray-Smith, D. J. (2015). Testing and validation of computer simulation models: Principles, methods and applications. Cham, Switzerland: Springer.

    Book  Google Scholar 

  • Nelles, O. (2001). Nonlinear system identification. Berlin, Germany: Springer.

    Book  Google Scholar 

  • Pack, A. I. (1976). Mathematical models of lung function. Ph. D. thesis, University of Glasgow, UK.

    Google Scholar 

  • Priestley, M. B. (1981). Spectral analysis and time series. San Diego, CA: Academic Press.

    MATH  Google Scholar 

  • Raol, J. R., Girija, G., & Singh, J. (2004). Modelling and parameter estimation of dynamic systems. IET Control Engineering Series No. 65. London, UK: IET.

    Google Scholar 

  • Rosenberg, J. R., Murray-Smith, D. J., & Rigas, A. (1982). An introduction to the application of system identification techniques to elements of the neuromuscular system. Transactions of the Institute of Measurement & Control, 4, 187–201.

    Article  Google Scholar 

  • Smith, M. I., Murray-Smith, D. J., & Hickman, D. (2007). Verification and validation issues in a generic model of electro-optic sensor systems. Journal of Defense Modeling and Simulation, 4, 17–27.

    Article  Google Scholar 

  • Söderström, T., & Stoica, P. (1989). System identification. New York, NY: Prentice-Hall.

    MATH  Google Scholar 

  • Tischler, M. B., & Remple, R. K. (2006). Aircraft and rotorcraft system identification. USA: AIAA.

    Google Scholar 

  • Thompson, K. R. (2009). Implementation of gaussian process models for non-linear system identification. Ph.D. thesis, University of Glasgow.

    Google Scholar 

  • Tomlinson, S. P., Tilley, D. G., & Burrows, C. R. (1994). Computer simulation of the human breathing process. IEEE Engineering in Medicine and Biology Magazine, 13, 115–124.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. School of Engineering, University of Glasgow, Rankine Building, Glasgow, G12 8QQ, UK

    David J. Murray-Smith

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  1. David J. Murray-Smith
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Correspondence to David J. Murray-Smith .

Editor information

Editors and Affiliations

  1. University of Bern, Bern, Switzerland

    Claus Beisbart

  2. Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany

    Nicole J. Saam

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Murray-Smith, D.J. (2019). The Use of Experimental Data in Simulation Model Validation. In: Beisbart, C., Saam, N. (eds) Computer Simulation Validation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-70766-2_15

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  • DOI: https://doi.org/10.1007/978-3-319-70766-2_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70765-5

  • Online ISBN: 978-3-319-70766-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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