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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Avila-Miranda, R. Begovich, O. Ruiz-Leon, J. |
| Copyright Year | 2013 |
| Description | Author affiliation: CINVESTAV-IPN, Guadalajara, Mexico (Avila-Miranda, R.; Begovich, O.; Ruiz-Leon, J.) |
| 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. |
| Starting Page | 779 |
| Ending Page | 782 |
| File Size | 729700 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781479904532 |
| e-ISBN | 9781479904549 |
| e-ISBN | 9781479904525 |
| DOI | 10.1109/CEC.2013.6557647 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-06-20 |
| Publisher Place | Mexico |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Green products Neural networks Fans Prediction algorithms Particle swarm optimization Kalman filters Greenhouse Particle Swarm Optimization Extended Kalman Filter Multilayer Recurrent Perceptron |
| Content Type | Text |
| Resource Type | Article |
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