Abstract
We discuss the problem of determining parameters in mathematical models described by ordinary differential equations. This problem is normally treated by least squares fitting. Here some results from nonlinear mean square approximation theory are outlined which highlight the problems associated with nonuniqueness of global and local minima in this fitting procedure. Alternatively, for Chebyshev fitting and for the case of a single differential equation, we extend and apply the theory of [17, 18] which ensures a unique global best approximation. The theory is applied to two numerical examples which show how typical difficulties associated with mean square fitting can be avoided in Chebyshev fitting.
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This paper is presented as an outcome of the LMS Durham Symposium convened by Professor C.T.H. Baker on 4th-14th July 1992 with support from the SERC under Grant reference number GR/H03964.
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Williams, J., Kalogiratou, Z. Least squares and Chebyshev fitting for parameter estimation in ODEs. Adv Comput Math 1, 357–366 (1993). https://doi.org/10.1007/BF02072016
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DOI: https://doi.org/10.1007/BF02072016
Keywords
- Parameter estimation
- least squares approximation
- Chebyshev approximation
- differential equations
- initial value problems