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On the identifiability and distinguishability of nonlinear parametric models

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  • Walter, Eric
  • Pronzato, Luc

Abstract

Testing parametric models for identifiability and distinguishability is important when the parameters to be estimated have a physical meaning or when the model is to be used to reconstruct physically meaningful state variables that cannot be measured directly. Examples are used to explain why and indicate briefly how, with special emphasis on nonlinear models.

Suggested Citation

  • Walter, Eric & Pronzato, Luc, 1996. "On the identifiability and distinguishability of nonlinear parametric models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 42(2), pages 125-134.
    • Handle: RePEc:eee:matcom:v:42:y:1996:i:2:p:125-134
    DOI: 10.1016/0378-4754(95)00123-9
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    References listed on IDEAS

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    1. Walter, Eric & Lecourtier, Yves, 1982. "Global approaches to identifiability testing for linear and nonlinear state space models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 24(6), pages 472-482.
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    Cited by:

    1. Xiaojun Liu & Arnaud Coutu & Stéphane Mottelet & André Pauss & Thierry Ribeiro, 2023. "Overview of Numerical Simulation of Solid-State Anaerobic Digestion Considering Hydrodynamic Behaviors, Phenomena of Transfer, Biochemical Kinetics and Statistical Approaches," Energies, MDPI, vol. 16(3), pages 1-31, January.
    2. Szép, Teodóra & van Cranenburgh, Sander & Chorus, Caspar G., 2022. "Decision Field Theory: Equivalence with probit models and guidance for identifiability," Journal of choice modelling, Elsevier, vol. 45(C).
    3. Mario Castro & Rob J de Boer, 2020. "Testing structural identifiability by a simple scaling method," PLOS Computational Biology, Public Library of Science, vol. 16(11), pages 1-15, November.
    4. Jane Teergele & Kourosh Danai, 2015. "Selection of outputs for distributed parameter systems by identifiability analysis in the time-scale domain," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(16), pages 2939-2954, December.
    5. Daniel Durstewitz, 2017. "A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements," PLOS Computational Biology, Public Library of Science, vol. 13(6), pages 1-33, June.
    6. Matthew S. Shotwell & Richard A. Gray, 2016. "Estimability Analysis and Optimal Design in Dynamic Multi-scale Models of Cardiac Electrophysiology," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 21(2), pages 261-276, June.

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