计算机工程与应用2018，Vol.54Issue(1)：54-59,69,7.DOI:10.3778/j.issn.1002-8331.1608-0261
土壤墒情预测自适应遗传神经网络算法研究
Research of adaptive genetic neural network algorithm in soil moisture prediction
摘要
Abstract
Forecasting soil moisture accurately is very important to monitor plant growing. Researchers are resorting to hybrid intelligence algorithms fusing more effective strategies into prediction process. Combination optimization can overcome the disadvantages of single method and improve predictive quality. This paper advances a novel algorithm to conquer the prematurity and sawtooth of traditional neural network. Firstly, it proposes the conception of genetic diversity function which measures genetic diversity of population. Secondly, it uses adaptive crossover strategy and mutation strategy to obtain the best initial weights and thresholds. Finally, it receives neural network results with better precision and efficiency and less iterations. Simulations reveal that in contrast to other genetic neural network, the quality of the soil moisture forecast has a great improvement in the new algorithm.关键词
人工智能算法/土壤墒情预测/自适应/遗传多样性函数/神经网络Key words
artificial intelligence algorithm/soil moisture prediction/adaptive/genetic diversity function/neural network分类
信息技术与安全科学引用本文复制引用
李宁,张琪,杨福兴,邓中亮..土壤墒情预测自适应遗传神经网络算法研究[J].计算机工程与应用,2018,54(1):54-59,69,7.基金项目
国家"十二五"科技支撑计划(No.2014BAD10B06). （No.2014BAD10B06）
