A greenhouse control with feed-forward and recurrent neural networks

被引：97

作者：

Fourati, Fathi ^{[1
]}

Chtourou, Mohamed ^{[1
]}

机构：

[1] Optimizat & Design Complex Syst, ENIS, ICOS, Sfax 3038, Tunisia

来源：

SIMULATION MODELLING PRACTICE AND THEORY | 2007年 / 15卷 / 08期

关键词：

greenhouse; neural networks; neural model; neural controller;

D O I：

10.1016/j.simpat.2007.06.001

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Greenhouses are classified as complex systems, so it is difficult to implement classical control methods for this kind of process. In our case we have chosen neural network techniques to drive the internal climate of a greenhouse. An Elman neural network has been used to emulate the direct dynamics of the greenhouse. Based on this model, a multilayer feed-forward neural network has been trained to learn the inverse dynamics of the process to be controlled. The inverse neural network has been placed in cascade with the neural model in order to drive the system outputs to desired values. Simulation results will be given to prove the performance of neural networks in control of the greenhouse. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：1016 / 1028

页数：13