Particle swarm optimization an alternative for parameter estimation in regression
Sergio G. de-los-Cobos-Silva: Autónoma Metropolitan Iztapalapa University
Antonio Terceño-Gómez: Rovira i Virgili University
Miguel Angel Gutiérrez-Andrade: Autónoma Metropolitan Iztapalapa University
Eric A. Rincón-García: Autónoma Metropolitan Azcapotzalco
Pedro Lara-Velázquez: Autónoma Metropolitan Azcapotzalco
Manuel Aguilar-Cornejo: Autónoma Metropolitan Iztapalapa University
- Fuzzy Economic Review: Volume XVIII, Number 2. 2013
- DOI: 10.25102/fer.2013.02.02
Abstract
The practice of applying curve fitting techniques to describe data is widespread in many fields: in biology, in medicine, in engineer, in economy, etc. This paper presents a heuristic technique named Particle Swarm Optimization to be used for parameter estimation in regression models. The algorithm was tested on 27 databases for nonlinear models and 11 for linear models by collection NIST (2001), which are considered with different degrees of difficulty. We present experimental results.