Variation function fitting method based on particle swarm optimization
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 convert...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2016-01-01
|
| Series: | MATEC Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/matecconf/20164402091 |
| Summary: | 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. |
|---|---|
| ISSN: | 2261-236X |