摘要
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.
出处
《广东农业科学》
CAS
CSCD
北大核心
2012年第13期60-62,69,共4页
Guangdong Agricultural Sciences
基金
国家"973"计划项目(2011CB302400)
国家自然科学基金(61175072
51165033
61163023)
关键词
遗传算法
模拟退火
神经网络
精确施肥
Genetic Algorithm
Simulated Annealing
neural network
precise fertilization