Application of Radial Basis Function Neural Networks to a Greenhouse Inside Air Temperature Model

Abstract

The problem of 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. This type of network is structurally simple and suitable to be integrated in real-time greenhouse environmental control systems. Due to the time variability of the process, training methods with on-line adaptation capabilities are neededThree of such methods are analysed in terms of fitness and network size. The model-predictive outputs obtained showed very close fittings to the measured values.