基于模拟退火和遗传算法的神经网络在精确施肥中的研究

Research based on the neural network of Simulated Annealing and Genetic Algorithm in the precise fertilization

为了更加精确地描述农作物产量与土壤和施肥量中的N、P、K浓度之间的复杂的非线性关系,对原始的BP神经网络进行了改进。首先采用模拟退火算法对神经网络的初始权值和阈值进行优化,提高了网络的整体逼近性能,再用遗传算法对神经网络的权值和阈值进行改善,并对这两种方法的优化效果进行了比较,结果表明模拟退火和遗传算法的神经网络能产生很好的效果。

In order to describe the complex nonlinear relationship between yield and the six factors, including soil nitrogen （N）, phosphorus （P）, potassium （K） concentration and N, P, K fertilizer input, we have improved the original BP neural network. Firstly, by revising the parameters, namely weight value and threshold value of ANN repeatedly with Simulated Annealing （SA）, the performance of ANN was improved tremendously. Secondly, we adopted Genetic Algorithm （GA） to improve the same parameters of the neural network. At last, we compared the performances between the two methods and made the conclusion that the BP neural network which is based on Simulated Annealing and Genetic Algorithm has a better performance.