Research Article Open Access

Curve Fitting Using Conic by Evolutionary Computing

Zainor Ridzuan Yahya, Abd Rahni Mt Piah and Ahmad Abd Majid

Abstract

Problem statement: A direct method, such as least squares technique is usually used to solve problems involving matching a curve or a surface to a set of data points. The solution obtained by this direct method is precise or very good in approximation, but computationally not very efficient. Thus, in this study, we propose an indirect approach using Particle Swarm Optimization (PSO) technique as an alternative. Approach: As a case study, we use conic curve which satisfy C0 continuity to be fitted to a given set of data points. PSO, a soft computing method is employed to optimize the control points and weights which are then used in conic equations. Results: Best fitted conic curve that represents all the given data points is then obtained. Conclusion: We use an indirect technique of soft computing methods, i.e., PSO to fit a curve to a given data set. We believe that other types of soft computing based heuristic procedures may also be used to solve related problems or to find its effectiveness.

Journal of Mathematics and Statistics
Volume 8 No. 1, 2012, 107-110

DOI: https://doi.org/10.3844/jmssp.2012.107.110

Submitted On: 4 June 2011 Published On: 7 February 2012

How to Cite: Yahya, Z. R., Piah, A. R. M. & Majid, A. A. (2012). Curve Fitting Using Conic by Evolutionary Computing. Journal of Mathematics and Statistics, 8(1), 107-110. https://doi.org/10.3844/jmssp.2012.107.110

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Keywords

  • Conics
  • curve fitting
  • particle swarm optimization method