人工神经网络在玉米气候适宜性评价中的应用

Application of Artificial Neural Network in the Climatic Suitability Evaluation of Maize

人工神经网络具有很强的自学习能力,善于联想、概括、类比和推理,能从大量统计资料中分析提取宏观统计规律,同时具有很强的鲁棒性、容错性以及强大并行计算能力,是进行非传统建模的有效工具。目前,应用比较多的BP神经网络,可通过学习以任意精度逼近任何连续映射,在农业工程项目中展示出了广阔的应用前景。主要介绍了人工神经网络的原理、特点及其在玉米气候适宜性评价中的应用。

Artificial neural network is an effective non-traditional modeling tool with a strong self-learning ability. It＇ s good at not only thinking, summarizing, analogizing, reasoning, but also analyzing and extracting the law of macrostatistics from a large number of statistical data. At the same time, it also has a strong robustness, fault tolerance as well as formidable parallel computing ability. At present, BP neural net- Work applied most can approach any continuous mapping through the study of the random precision and demonstrate a broad application prospects in agricultural projects. The principles and characteristics of artificial neural networks and the applications in the climate suitability evaluation of maize were mainly introduced.