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Characterisation of the iterative integral parameter identification method

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Abstract

Parameter identification methods are used to find optimal parameter values to fit models to measured data. The single integral method was defined as a simple and robust parameter identification method. However, the method did not necessarily converge to optimum parameter values. Thus, the iterative integral method (IIM) was developed. IIM will be compared to a proprietary nonlinear-least-squares-based Levenberg–Marquardt parameter identification algorithm using a range of reasonable starting values. Performance is assessed by the rate and accuracy of convergence for an exemplar two parameters insulin pharmacokinetic model, where true values are known a priori. IIM successfully converged to within 1% of the true values in all cases with a median time of 1.23 s (IQR 0.82–1.55 s; range 0.61–3.91 s). The nonlinear-least-squares method failed to converge in 22% of the cases and had a median (successful) convergence time of 3.29 s (IQR 2.04–4.89 s; range 0.42–44.9 s). IIM is a stable and relatively quick parameter identification method that can be applied in a broad variety of model configurations. In contrast to most established methods, IIM is not susceptible to local minima and is thus, starting point and operator independent.

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Canterbury, PO Box:4800, Christchurch, 8140, New Zealand

    Paul D. Docherty, J. Geoffrey Chase & Timothy David

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  1. Paul D. Docherty
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  2. J. Geoffrey Chase
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  3. Timothy David
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Correspondence to Paul D. Docherty.

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Docherty, P.D., Chase, J.G. & David, T. Characterisation of the iterative integral parameter identification method. Med Biol Eng Comput 50, 127–134 (2012). https://doi.org/10.1007/s11517-011-0851-y

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  • DOI: https://doi.org/10.1007/s11517-011-0851-y

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