干旱区生态环境知识资源中心(Arid): 基于Elman神经网络的阿拉善荒漠啮齿动物群落组成物种数量预测研究

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	基于Elman神经网络的阿拉善荒漠啮齿动物群落组成物种数量预测研究
其他题名	Prediction of the Number of Rodent Community Composition Species Based on Elman Neural Network in Alasan Desert
	卢志宏 1; 武晓东 1; 郭利彪 2; 付和平 1; 满都呼 1; 岳闯 1; 柴享贤 1; 包达尔罕 1; 杨素文 1; 叶丽娜 1; 李燕妮 1
来源期刊	生态环境学报
ISSN	1674-5906
出版年	2015
卷号	24 期号:12 页码:1976-1982
中文摘要	群落的数量变动及预测是生态学研究的重要内容,将神经网络技术应用到啮齿动物群落数量预测中是一种新尝试。Elman神经网络通过在前馈网络中增加延时算子,实现了记忆功能,能够对啮齿动物组成物种数量进行动态模拟和预测。以腾格里沙漠东缘荒漠为试验区,以啮齿动物数量为研究对象,采用标志重捕法,逐月监测2006─2014年每年的4─10月捕获量,建立Elman神经网络预测模型,利用2006─2013年的捕获量建立训练网络,以2014年的数据进行验证与测试,比较3种数据处理方法建立预测模型后的平均误差和拟合度,确立最优模型,预测阿拉善荒漠啮齿动物组成物种数量动态。结果表明:(1)未经归一化处理预测结果的平均误差mse为5.30,最小误差1.52%,拟合度为0.80;(2)经[0, 1]归一化处理的预测结果平均误差mse为4.51,最小误差1.54%,拟合度为0.82;(3)经[-1, 1]归一化处理预测结果的平均误差mse为5.03,最小误差1.63%,拟合度为0.69;(4)3种归一化处理后Elman神经网络模型差异不显著。通过平均误差和拟合度的比较,文章认为采用[0, 1]归一化建立的Elman神经网络能较好的预测荒漠啮齿动物数量的变化规律,应用该网络可以预测阿拉善荒漠啮齿动物组成物种数量变化趋势,对指导当地鼠情监控和防治具有重要意义。
英文摘要	The fluctuation and prediction of population is one of important research contents in ecology, however, it is necessary to explore new approaches. In this study, a novel method was used in rodent population prediction by neural network technology. The neural network of Elmam has the function of memory, which can simulate and forecast the quantity of species in rodent by adding delay-units in feedforward networks. Based on the eastern edge of Tengger desert as the study area, with the rodent population as the research object, by use of mark recapture method to continuously looked into 2006─2014 (Apr-Oct) and build Elman neural network forecasting model, data between 2006─2013 were used to build training network, data of 2014 were used for test, the mean error and fitting degree of three processing methods were compared and predicted the number of Alashan desert rodent population dynamics. Results showed that:(1) Average error of the prediction results without normalization mse was 5.30 with Minimum error of 1.52%, fitting degree 0.80. (2) After [0, 1] normalization, Average error of the prediction results mse was 4.51 with Minimum error of 1.54%, fitting degree 0.82. (3) After [-1, 1] normalization, Average error of the prediction results mse was 5.03 with Minimum error of 1.63%, fitting degree 0.69. (4) The difference of Elman neural network model was not significant after three kinds of normalized treatment. While comparison of mean error and fitting degree, the article suggested that by use of [0, 1] normalization to establish Elman neural network can better predicted the animal composition and species dynamic trend. Thus providing theoretical basis for guiding and preventing local rodent infestation.
中文关键词	Elman神经网络 ; 阿拉善荒漠 ; 啮齿动物 ; 标志重捕法
英文关键词	elman neural network alasan desert rodent mark recapture method
语种	中文
国家	中国
收录类别	CSCD
WOS类目	FORESTRY ; ENVIRONMENTAL SCIENCES
WOS研究方向	Forestry ; Environmental Sciences & Ecology
CSCD记录号	CSCD:5614537
来源机构	内蒙古农业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/233123
作者单位	1.内蒙古农业大学生态环境学院, 草业与草地资源教育部重点实验室, 呼和浩特, 内蒙古 010018, 中国; 2.中国农业科学研究院草原科学研究所, 呼和浩特, 内蒙古 010010, 中国
推荐引用方式 GB/T 7714	卢志宏,武晓东,郭利彪,等. 基于Elman神经网络的阿拉善荒漠啮齿动物群落组成物种数量预测研究[J]. 内蒙古农业大学,2015,24(12):1976-1982.
APA	卢志宏.,武晓东.,郭利彪.,付和平.,满都呼.,...&李燕妮.(2015).基于Elman神经网络的阿拉善荒漠啮齿动物群落组成物种数量预测研究.生态环境学报,24(12),1976-1982.
MLA	卢志宏,et al."基于Elman神经网络的阿拉善荒漠啮齿动物群落组成物种数量预测研究".生态环境学报 24.12(2015):1976-1982.