Application of radial basis function neural networks to a greenhouse inside air temperature model

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.

Predicting the greenhouse inside air temperature with RBF neural networks

The application of the Radial Basis Function (RBF) Neural Network (NN) to greenhouse inside air temperature modelling has been previously investigated (Ferreira et al., 2000a). In those studies, the inside air temperature is modelled as a function of the inside relative humidity and of the outside temperature and solar radiation. A second-order model structure previously selected (Cunha et al., 1996) in the context of dynamic temperature models identification, is used.

Real-time parameter estimation of dynamic temperature and humidity models for greenhouse adaptive climate control

A real-time parameter estimator for the climate discrete-time dynamic models of a greenhouse located at the North of Portugal are presented. The experiments showed that the second order models identified for the air temperature and humidity achieve a close agreement between simulated and experimantal data.

Real-time parameter estimation of dynamic temperature models for greenhouse environmental control

For a greenhouse located at UTAD-University, the methods used to estimate in real-time the parameters of the inside air temperature model will be described. The structure and the parameters of the climate discrete-time dynamic model were previously identified using data acquired during two different periods of the year.

Dynamic temperature models of a soiless greenhouse

In this paper climate discrete-time dynamic models for the inside air temperature of a soilless greenhouse are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity.

A greenhouse climate multivariable predictive controller

A multivariable predictive controller was implemented to regulate the air temperature, humidity and CO2 concentration for a greenhouse located in the north of Portugal. The controller outputs are computed in order to optimise the future behaviour of the greenhouse environment, concerning the set-point accuracy and the minimization of energy inputs.

Comparison of on-line learning algorithms for RBF models in greenhouse environmental control problems

The problem with the adequacy of radial basis function neural networks to model the inside air temperature as a function of the outside air temperature and solar radiation, and the inside relative humidity in an hydroponic greenhouse is addressed.

Identification of greenhouse climate dynamic models

In this paper climate discrete-time dynamic models for the inside air temperature of two different greenhouses are identified, using data acquired during two different periods of the year. These models employ data from air temperature and relative humidity (both outside and inside the greenhouse), solar radiation, wind speed, and control inputs (ventialtion, etc.).

Modelos e estratégias de controlo ambientais para estufas agrícolas

For a greenhouse with a double polyethylene cover, it will be presented a dynamic climate transfer function and an adaptive controller for the air temperature. The model employ data acquired from the outside weather and from the heating and cooling inputs.

Long-term seasonal changes in the abundance of Dendrodoris nudibranchs: a five-year survey

Dissertação de mestrado, Biologia Marinha, Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2015