Please use this identifier to cite or link to this item: http://cio.repositorioinstitucional.mx/jspui/handle/1002/951
A hierarchical genetic algorithm approach for curve fitting with B-splines
FRANCISCO JAVIER CUEVAS DE LA ROSA
En Embargo
18-09-2019
Atribución-NoComercial-SinDerivadas
Automatic curve fitting using splines has been widely used in data analysis and engineering applications. An important issue associated with data fitting by splines is the adequate selection of the number and location of the knots, as well as the calculation of the spline oefficients. Typically, these parameters are estimated separately with the aim of solving this non-linear problem. In this paper, we use a hierarchical genetic algorithm to tackle the B-spline curve fitting problem. The proposed approach is based on a novel hierarchical gene structure for the chromosomal representation, which allows us to determine the number and location of the knots, and the B-spline coefficients automatically and simultaneously. Our approach is able to find optimal solutions with the fewest parameters within the B-spline basis functions. The method is fully based on genetic algorithms and does not require subjective parameters like smooth factor or knot locations to perform the solution. In order to validate the efficacy of the proposed approach, simulation results from several tests on smooth functions and comparison with a successful method from the literature have been included.
17-09-2014
Artículo
CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA
Appears in Collections:Articulos Arbitrados 2014