this paper nonlinear data fitting problems which have as their underlying model a linear combination of nonlinear functions. More generally, one can also consider that there are two sets of unknown parameters, where one set is dependent on the other and can be explicitly eliminated. Models of this type are very common and we will show a variety of applications in different fields. Inasmuch as many inverse problems can be viewed as nonlinear data fitting problems, this material will be of interest to a wide cross-section of researchers and practitioners in parameter, material or system identification, signal analysis, the analysis of spectral data, medical and biological imaging, neural networks, robotics, telecommunications and model order reduction, to name a few

Splines with free knots have been extensively studied in regard to calculating the optimal knot positions. The dependence of the accuracy of approximation on the knot distribution is highly nonlinear, and optimisation techniques face a difficult problem of multiple local minima. The domain of the problem is a simplex, which adds to the complexity. We have applied a recently developed cutting angle method of deterministic global optimisation, which allows one to solve a wide class of optimisation problems on a simplex. The results of the cutting angle method are subsequently improved by local discrete gradient method. The resulting algorithm is sufficiently fast and guarantees that the global minimum has been reached. The results of numerical experiments are presented.

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Principal differential analysis (PDA) is an alternative parameter estimation technique for differential equation models in which basis functions (e.g., B-splines) are fitted to dynamic data. Derivatives of the resulting empirical expressions are used to avoid solving differential equations when estimating parameters. Benefits and shortcomings of PDA were examined using a simple continuous stirred-tank reactor (CSTR) model. Although PDA required considerably less computational effort than traditional nonlinear regression, parameter estimates from PDA were less precise. Sparse and noisy data resulted in poor spline fits and misleading derivative information, leading to poor parameter estimates. These problems are addressed by a new iterative algorithm (iPDA) in which the spline fits are improved using model-based penalties. Parameter estimates from iPDA were unbiased and more precise than those from standard PDA. Issues that need to be resolved before iPDA can be used for more complex models are discussed.