基于模糊RBF神经网络算法的灌溉控制系统设计

对于一个农田占地面积大,灌溉用水量少的农业国而言,高效用水影响着农业的稳步发展;针对这一现象,提出一种基于模糊RBF神经网络的灌溉策略,将隐层的输出函数构成一组基来逼近目标函数,预测出灌溉的需水量,并用模糊控制计算出具体的灌溉时间;以山西省忻州地区种植的玉米利合328号为例,对气象条件、土壤条件以及作物特性进行分析,可以根据不同环境计算出灌溉时间;对大田采取高效、实时的灌溉策略,不仅可以提高灌溉用水的利用率,还可以为大田灌溉的发展奠定基础,增加了作物的产量。

Irrigation Strategy Based on Fuzzy RBF Neural Network

For an agricultural country with a large area of farmland and little water for irrigation, efficient water use affects the steady development of agriculture.Aiming at this phenomenon, an irrigation strategy based on fuzzy RBF neural network is proposed, which USES the output function of the hidden layer to form a basis to approximate the objective function, predicts the irrigation water demand, and calculates the specific irrigation time by fuzzy control.The meteorological conditions, soil conditions and crop characteristics of lihe 328 corn planted in xinzhou, Shanxi Province were analyzed, irrigation time can be calculated according to different environments..The efficient and real-time irrigation strategy can not only improve the utilization rate of irrigation water, but also lay a foundation for the development of field irrigation and increase the yield of crops.