Welcome to the InfoSci Platform
Reference Hub3
Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate

M. Outanoute, A. Lachhab, A. Selmani, H. Oubehar, A. Snoussi, M. Guerbaoui, A. Ed-dahhak, B. Bouchikhi
Copyright: © 2018 |Volume: 16 |Issue: 1 |Pages: 10
ISSN: 1539-2937|EISSN: 1539-2929|EISBN13: 9781522542360|DOI: 10.4018/JECO.2018010106
Cite Article Cite Article

MLA

Outanoute, M., et al. "Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate." JECO vol.16, no.1 2018: pp.72-81. http://doi.org/10.4018/JECO.2018010106

APA

Outanoute, M., Lachhab, A., Selmani, A., Oubehar, H., Snoussi, A., Guerbaoui, M., Ed-dahhak, A., & Bouchikhi, B. (2018). Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate. Journal of Electronic Commerce in Organizations (JECO), 16(1), 72-81. http://doi.org/10.4018/JECO.2018010106

Chicago

Outanoute, M., et al. "Particle Swarm Optimization of BP-ANN Based Soft Sensor for Greenhouse Climate," Journal of Electronic Commerce in Organizations (JECO) 16, no.1: 72-81. http://doi.org/10.4018/JECO.2018010106

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

In this article, the authors develop the Particle Swarm Optimization algorithm (PSO) in order to optimise the BP network in order to elaborate an accurate dynamic model that can describe the behavior of the temperature and the relative humidity under an experimental greenhouse system. The PSO algorithm is applied to the Back-Propagation Neural Network (BP-NN) in the training phase to search optimal weights baded on neural networks. This approach consists of minimising the reel function which is the mean squared difference between the real measured values of the outputs of the model and the values estimated by the elaborated neural network model. In order to select the model which possess higher generalization ability, various models of different complexity are examined by the test-error procedure. The best performance is produced by the usage of one hidden layer with fourteen nodes. A comparison of measured and simulated data regarding the generalization ability of the trained BP-NN model for both temperature and relative humidity under greenhouse have been performed and showed that the elaborated model was able to identify the inside greenhouse temperature and humidity with a good accurately.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.