African Journal of
Agricultural Research

  • Abbreviation: Afr. J. Agric. Res.
  • Language: English
  • ISSN: 1991-637X
  • DOI: 10.5897/AJAR
  • Start Year: 2006
  • Published Articles: 6578

Full Length Research Paper

Modelling greenhouse air temperature using evolutionary algorithms in auto regressive models

R. Guzmán-Cruz
  • R. Guzmán-Cruz
  • División de Investigación y Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Col. Las Campanas, C.P. 76010 Querétaro, Qro., México.
  • Google Scholar
E. Olvera-González
  • E. Olvera-González
  • Universidad Autónoma de Zacatecas, Jardín Juárez 147, Centro Histórico, Zacatecas, Zacatecas, México.
  • Google Scholar
I. L. López-Cruz
  • I. L. López-Cruz
  • Posgrado en Ingeniería Agrícola y Uso Integral del Agua, Universidad Autónoma de Chapingo, C.P. 056230 Chapingo, Méx., México.
  • Google Scholar
R. Montoya-Zamora
  • R. Montoya-Zamora
  • División de Investigación y Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, Col. Las Campanas, C.P. 76010 Querétaro, Qro., México.
  • Google Scholar


  •  Accepted: 03 September 2012
  •  Published: 31 January 2013

Abstract

 

This paper presents comparison of genetic algorithms (GAs) and evolutionary programming (EP) to estimate parameters of a linear auto regressive model with external input (ARX) and an auto regressive moving average model with external input structures (ARMAX) that predict the behavior of air temperature within a greenhouse. Data groups were used to estimate and validate models and these data groups were 20:80, 33.33:66.67, 50:50, 66.67:33.33 and 80:20%. The objective was to determine which evolutionary algorithm generates parameter values that give the best prediction of the environment in a greenhouse located in the central region of Mexico. Simulation and analysis of the ARX and ARMAX model’s performance show that these models under-estimate measurements. Furthermore, the estimations of the inside temperature have a better fit when the parameter identification of an ARX structure is calculated by means of GAs, so that, there is a better fit of the simulated data to measured data when the 20% of the data are used to estimate and 80% of the data are used to validate the model.

 

Key words: Auto regressive moving average model with external input structures (ARMAX) model, auto regressive model with external input (ARX) model, genetic algorithms, evolutionary programming, parameter identification.