PARTICLE SWARM OPTIMIZATION: AN ALTERNATIVE FOR PARAMETER ESTIMATION IN REGRESSION

S.G. de-los-Cobos-Silva, A. Terceño-Gómez, M.A. Gutiérrez-Andrade, E.A. Rincón-García, P. Lara-Velázquez and M. Aguilar-Cornejo
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S.G. de-los-Cobos-Silva: Autónoma Metropolitan Iztapalapa University, Electronic Engineering Department, Iztapalapa, Mexico D.F.
A. Terceño-Gómez: Rovira i Virgili University, Business Administration Department, Faculty of Business and Economics, Reus, Spain
M.A. Gutiérrez-Andrade: Autónoma Metropolitan Iztapalapa University, Electronic Engineering Department, Iztapalapa, Mexico D.F.
E.A. Rincón-García: Autónoma Metropolitan Azcapotzalco University, Computer Engineering Department, Av. San Pablo 180, Colonia Reynosa Tamauluipas, Mexico D.F.,
P. Lara-Velázquez: Autónoma Metropolitan Azcapotzalco University, Computer Engineering Department, Av. San Pablo 180, Colonia Reynosa Tamauluipas, Mexico D.F.,
M. Aguilar-Cornejo: Autónoma Metropolitan Iztapalapa University, Electronic Engineering Department, Iztapalapa, Mexico D.F.

Fuzzy Economic Review, 2013, vol. XVIII, issue 2, 19-32

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

Keywords: particle swarm optimization; regression (search for similar items in EconPapers)
JEL-codes: C02 C61 C65 (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:fzy:fuzeco:v:xviii:y:2013:i:2:p:19-32

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