Title :
An optimal and intelligent control strategy to ventilate a greenhouse
Author :
Avila-Miranda, Raul ; Begovich, O. ; Ruiz-Leon, Javier
Author_Institution :
CINVESTAV-IPN, Guadalajara, Mexico
Abstract :
In this paper, it is presented an optimal and intelligent technique to ventilate a greenhouse during the day. This technique is the result of the combination of a neural network and the particle swarm optimization algorithm. First, predictions on the dynamic behavior of the system variables are computed by means of a multilayer recurrent perceptron, trained with an extended Kalman filter. Then, using these predictions and the particle swarm optimization algorithm, we calculate the time instants when the fans of the greenhouse must be turn on and off, in order to eliminate the unwanted excess of temperature and at the same time minimizing the time lapse where the fans remain turned on. The algorithm performance is shown through simulation.
Keywords :
Kalman filters; greenhouses; learning (artificial intelligence); minimisation; multilayer perceptrons; nonlinear filters; particle swarm optimisation; recurrent neural nets; ventilation; dynamic system variable behavior prediction; excess temperature elimination; extended Kalman filter; greenhouse fans; greenhouse ventilation; intelligent control strategy; multilayer recurrent perceptron training; neural network; optimal control strategy; particle swarm optimization algorithm; time instants; time lapse minimization; Fans; Green products; Kalman filters; Neural networks; Particle swarm optimization; Prediction algorithms; Extended Kalman Filter; Greenhouse; Multilayer Recurrent Perceptron; Particle Swarm Optimization;
Conference_Titel :
Evolutionary Computation (CEC), 2013 IEEE Congress on
Conference_Location :
Cancun
Print_ISBN :
978-1-4799-0453-2
Electronic_ISBN :
978-1-4799-0452-5
DOI :
10.1109/CEC.2013.6557647
