摘要
针对以往智能灌溉系统多采用一级模糊控制且专家经验难以结合多种环境因素,导致适用范围小,不能精准灌溉等问题,设计了一种基于改进Elman神经网络和模糊控制的智能灌溉系统。根据作物的种类和种植时间,通过一级模糊控制器得到最佳土壤湿度;根据土壤湿度差值和蒸发蒸腾量,通过二级模糊控制器得到,灌溉决策结果,从而达到精准灌溉的目的。传统的蒸发蒸腾量计算方式比较复杂,而Elman神经网络具有稳定性强,动态性好的特点,利用改进粒子群优化(IPSO)算法对其优化,解决其容易陷入局部最优和收敛速度慢的问题,该神经网络在作物蒸发蒸腾量的预测方面有较好的效果。实验表明,该系统运行稳定,可实现精准灌溉。
In the past,intelligent irrigation systems mostly used first-level-fuzzy control and expert experience was difficult to combine various environmental factors,resulting in small application scope and inaccurate irrigation.An intelligent irrigation system based on improved Elman neural network and fuzzy control was designed.According to the type of crop and planting time,the optimal soil moisture is obtained through the,first-level fuzzy controller;according to the humidity difference and evapotranspiration,the irrigation decision result is obtained through the second-level fuzzy controller,so as to achieve the purpose of precise irrigation.The traditional evapotranspiration calculation method is more complicated,and the Elman neural network has the characteristics of strong stability and good dynamics.The IPSO algorithm is used to optimize it to solve the problem of easy to fall into the local optimum and slow convergence.There are good results in the prediction of crop evapotranspiration.Experiments show that the system runs stably and can achieve precise irrigation.
作者
张明岳
李丽敏
温宗周
张顺锋
Zhang Mingyue;Li Limin;Wen Zongzhou;Zhang Shunfeng(College of electronic information,Xi'an Polytechnic University,Xi'an 710600,China)
出处
《国外电子测量技术》
北大核心
2021年第11期155-160,共6页
Foreign Electronic Measurement Technology
基金
陕西省重点研发计划一般项目(2021NY-192)资助。
