[1]郝仕龙,柯俊,李壁成,等.基于人工神经网络的农户经济收入预测研究[J].水土保持研究,2005,12(03):117-119.
HAO Shi-long,KE Jun,LI bi-cheng,et al.Prediction Study of Farmer Income Based on the Artificial Neural Network[J].Research of Soil and Water Conservation,2005,12(03):117-119.
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《水土保持研究》[ISSN:1005-3409/CN:61-1272/P]
卷:
12
期数:
2005年03期
页码:
117-119
栏目:
出版日期:
1900-01-01
- Title:
-
Prediction Study of Farmer Income Based on the Artificial Neural Network
- 作者:
-
郝仕龙1,2, 柯俊2, 李壁成1, 赵小敏2
-
1. 中国科学院水利部水土保持研究所, 陕西杨陵 712100;
2. 江西农业大学国土与资源环境学院, 南昌 330045
- Author(s):
-
HAO Shi-long1,2, KE Jun2, LI bi-cheng1, ZHAO Xiao-min2
-
1. Institute of Soil and Water Conservation, Chinese Academy ofScience and Ministry of Water resources, Yangling, Shaanxi 712100, China;
2. College of Land Resource and Environment, Jiangxi Agricultural University, Nanchang 330045, China
-
- 关键词:
-
人工神经网络; BP算法; 农户经济收入预测
- Keywords:
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artificial neural network; BP model; farmer income prediction
- 分类号:
-
F323.15
- 摘要:
-
研究了利用误差反向传播人工神经网络(BP网络)的多变量综合预测问题,并以研究上黄试区民户经济收入为背景,建立了相应的多变量综合预测BP模型。预测结果表明农户经济收入神经网络模型预测精度较高,开辟了农户经济收入预测的有效途径。
- Abstract:
-
The multiple variable synthetical predication problems with error back propagation training artificial neural network (BP neural network) is researched. To study the farmer income of Shanghuang experimental area is the background, BP’s models of multiple variable synthetical predication were established. The results of experimental prediction show that the accuracy of predication by the neural network models is very high. They open up a new way to predict farmer income.
参考文献/References:
[1] 孙会君,王新华.应用人工神经网络确定评价指标的权重[J].山东科技大学学报,2001,20(3):84-86.
[2] 杨建刚,等.利用结构化神经网络识别振动系统非线性特性[J].振动工程学报,1995,8(3):25-29.
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[5] 潘大丰,何书金,郭焕成.矿区废弃土地资源适宜性评价[J].地理科学进展,1998,17(4):40-45.
[6] 楼顺天.基于MATLAB的系统分析与设计-神经网络[M].西安:西安电子科技大学出版社,1998.
[7] 高隽.人工神经网络原理及住址实例[M].北京:机械工业出版社,2003.
[8] 施鸿宝.神经网络及其应用[M]. 西安:西安交通大学出版社,1999.
相似文献/References:
[1]张斌,刘俊民.基于BP神经网络的地下水动态预测[J].水土保持研究,2012,19(05):235.
ZHANG Bin,LIU Jun-min.Prediction of Groundwater Dynamics Based on the BP Neural Network[J].Research of Soil and Water Conservation,2012,19(03):235.
更新日期/Last Update:
1900-01-01