MATEC Web of Conferences
Volume 44, 20162016 International Conference on Electronic, Information and Computer Engineering
|Number of page(s)||4|
|Section||Electronics, Information and Engineering Application|
|Published online||08 March 2016|
Variation function fitting method based on particle swarm optimization
School of Tourism and Geographical Sciences of Yunnan Normal University, Kunming, 650050, China
2 Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China
3 State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, 210023, China
4 Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China
a Corresponding author: firstname.lastname@example.org
In the Kriging interpolation method, different theory models of variation function are selected and fitted. There are many common variation function models, such as spherical model, index model, Gaussian model and so on. As these variation function models are non-linear, non-linear model are converted to linear model when these variation function models are solved. Different variation function models with different conversion methods are lack of generality in the process of Kriging interpolation. Particle swarm optimization algorithm with the advantages of global optimal solution can be directly used to solve non-linear fitting equation. In this paper, variation function model based on particle swarm optimization algorithm is fitted. Experiment shows that it is appropriate for fitting variable function based particle swarm optimization algorithm.
© Owned by the authors, published by EDP Sciences, 2016
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