Modeling of the Growing Process of Tomato Based on Modified Elman Network and FGA

The paper focuses on the modeling of the growing process of tomato planted in greenhouse. The modified Elman network and fuzzy genetic algorithm are used for modeling in this paper. The growing process of tomato planted in greenhouse has some features as control variables being complex . the growing process being randomicity, nonlinear and changing all of a sudden, etc.. One of the main apparatus, caudexes is chosen as the object. The solar radiation, the temperature, humidity and carbon dioxide strength are chosen from all influential factors as control input according to the experts´ advices. According to the characters, modified Elman network is used for modeling because of its good performance in dynamic system identification. And fuzzy genetic algorithm is used for learning the parameters of neural network. Results of the simulations show that the model based on modified Elman network is of better performance than those based on other method.