The Bp Neural Network Simulation Of Multi-span Greenhouse Environmental Factors Under Ventilation Conditions In Summer In Central China

Greenhouse horticulture is one of the most mature ways of agricultural production methods. It can provide relatively independent environmental systems and a good environment condition to overcome the impact of outside environment on crop growth, and to keep good annual crop production. In order to achieve better greenhouse production, to keep accurate control of the greenhouse environment has attracted more and more attention. Artificial neural network, which has good self-adaptability, self-organization, strong learning, fault-tolerance and anti-jamming capability; can be flexible on the complexity of multi-factor unknown coefficients model establishment. Because traditional methods of mathematical model can not solve these problems, the neural network methods is used to design BP neural network to establish 3 multi-span plastic greenhouse environmental model for its good prerequisite for accurately controlling the greenhouse environment.To provided a theoretical basis for the control of the greenhouse environment the impact of environmental factors on greenhouse was studied. The results indicated that the changes of environmental factors are complex under different working conditions in greenhouse. Owing to its no regularity, a simple mathematical method could not be used to accurately descript the various environmental factors in the greenhouse. The relationship between different environmental factors was complex and changeable, which was more difficult to establish the greenhouse mathematic model with interactions of environmental factors.The thesis took the plastic greenhouse in the Institute of engineering and technology of Huazhong Agricultural University as the research object, and obtained the test data from the 4 factors two-level orthogonal test. This test took the environmental parameters outside greenhouse as input parameters, and took the environment parameters inside greenhouse which needed for controlling as preparative parameters. These parameters were used to establish the forecast BP neural network model. The original data was pretreated, and the neural network model with different network structures and algorithms were comprehensive compared. The neural network had a better predictability and a good network generalization ability when the neural network structure was 9-11-5 and the network algorithm was traingdx. The main MATLAB neural network toolbox program code was provided in this paper. In order to verify the accuracy and practicality of the prediction system, the 80 group of environment test data outside and inside greenhouse were selectd to treat the model with simulations and the generalization, the results of the prediction model and actual test data were compared. It showed that the prediction result was good, and the traingdx had good generalization ability. The forecasting system has reference value for the environmental control of greenhouse in the middle of China.