Curve fitting and identification of physical spectra

Böckmann, Christine

doi:10.1016/0377-0427(95)00202-2

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…Atieg and Watson [20] made a summary and comparison of some methods which mainly include Newton method and GN method. Böckmann [21] proposed a modification of the trust-region Gauss-Newton method with a two-parameter approximation for a sum of weighted functions. Nyarko and Scitovski [22] gave a method for solving the parameter identification problem for ordinary second order differential equations using genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Application of gradient descent method to the sedimentary grain-size distribution fitting

Chen

Qiao

2009

Journal of Computational and Applied Mathematics

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a b s t r a c tExistence of a least squares solution for a sum of several weighted normal functions is proved. The gradient descent (GD) method is used to fit the measured data (i.e. the laser grain-size distribution of the sediments) with a sum of three weighted lognormal functions. The numerical results indicate that the GD method is not only easy to operate but also could effectively optimize the parameters of the fitting function with the error decreasing steadily. Meanwhile the overall fitting results are satisfactory. As a new way of data fitting, the GD method could also be used to solve other optimization problems.

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Section: Introductionmentioning

confidence: 99%