基于改进遗传算法优化BP神经网络的土壤湿度预测模型

我国是农业大国,在进行农业生产过程中,对土壤的湿度进行精准预测具有非常重要的意义.针对传统BP(back propagation)神经网络在预测过程中会出现局部最小化以及收敛速度慢的问题,本文将改进的遗传算法(genetic algorithm)应用到传统BP神经网络模型当中,提出了一种自适应遗传算法优化BP神经网络的...

Optimized BP Neural Network Model Based on Improved Genetic Algorithm for Soil Moisture Prediction

China is a large agricultural country. In the process of agricultural production, it is of great significance to accurately predict the soil moisture. In view of the local minimization and slow convergence in the prediction process of the traditional back propagation (BP) neural network, an improved genetic algorithm is applied to the traditional BP neural network model in this study. A soil moisture prediction method is proposed that optimizes the BP neural network by the adaptive genetic algorithm. A prediction model of the BP neural network optimized by the improved genetic algorithm is established by the Matlab simulation software and experimented on the soil moisture of corn fields in Harbin. The results show that the accuracy of the model is higher than that of the unoptimized BP neural network model. This model can greatly reduce the use of moisture sensor and thus reduce the agricultural production cost.