Identification of linear and non linear curve fitting models using particle swarm optimization algorithm

2020 ◽  
Author(s):  
P. Syamala Rao ◽  
G. Parthasaradhi Varma ◽  
Ch. Durga Prasad
Keyword(s):  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Non Linear

B-Spline Curve Fitting Based on Adaptive Particle Swarm Optimization Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1299-1304 ◽  
Author(s):  
Yue Hong Sun ◽  
Zhao Ling Tao ◽  
Jian Xiang Wei ◽  
De Shen Xia
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Control Points ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curve

For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.

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