基于RBF神经网络的温室温度调控研究

A Research of Temperature Adjustment of Glasshouse Crops Based on RBF Neural Network

根据光合作用对温室环境因子的非线性,结合RBF神经网络对非线性的良好辨识能力,研究出一种温度调控技术。结合温室光照、温度变化规律,运用RBF神经网络建立温室生菜光合速率与二者的量化模型,通过生菜的光合作用速率来衡量生菜生长状况,在温室小气候里实现对生菜产量的量化控制。该模型预测精度较高,可作为温室测控系统环境因子调控依据。

According to. the nonlinear of greenhouse photosynthesis to environmental factors, combined with RBF network excellent ability identifying non -linear models, a new temperature fertilization technique was brought forward. Combining the diversification rule of greenhouse illumination and temperature, as well as the growth of greenhouse lettuce, using RBF neural network, a quantitative model between greenhouse lettuce photosynthetic rate and the two environment factors was built. That can predict lettuce photosynthetic rates with different environmental conditions to measure the growth rate of greenhouse lettuce, accordingly to realize the control of the production of lettuce quantitative in the microclimate. The prediction model has a higher accuracy, which provides theories for the decision - making of environmental factors adjustment in greenhouse control system.