| 人工神经网络在流域植被类型优化中的应用 |
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| 引用本文： | 胡淑萍,余新晓,王小平,秦永胜,陈俊崎.人工神经网络在流域植被类型优化中的应用[J].北京林业大学学报,2008(Z2). |
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| 作者姓名： | 胡淑萍 余新晓 王小平 秦永胜 陈俊崎 |
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| 作者单位： | 水土保持与荒漠化防治教育部重点实验室北京林业大学水土保持学院;北京市园林绿化国际项目合作管理办公室; |
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| 基金项目： | “十一五”国家科技支撑计划项目(2006BAD03A02) |
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| 摘 要： | 鉴于防护林体系植被类型受众多因素影响,并具有复杂的非线性特点,以近自然林业为理念,采用LM人工神经网络建立防护林体系植被类型优化模型,并用典型流域的现实数据对模型进行训练和预测验证。结果表明,用Matlab构建的模型方便、快捷,同时具有收敛速度快、预测精度高的特点,为防护林体系植被优化提供了一种行之有效的方法。
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| 关 键 词： | 植被类型 优化 人工神经网络 LM算法 |
Application of artificial neural network in optimization of vegetation types in watershed |
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| HU Shu-ping,YU Xin-xiao,WANG Xiao-ping,QIN Yong-sheng,CHEN Jun-qi. Key Laboratory of Soil , Water Conservation & Desertification Combating of Ministry of Education,School of Soil , Water Conservation,Beijing Forestry University,P.R.China, Beijing Forestry , Parks.Application of artificial neural network in optimization of vegetation types in watershed[J].Journal of Beijing Forestry University,2008(Z2). |
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| Authors: | HU Shu-ping YU Xin-xiao WANG Xiao-ping QIN Yong-sheng CHEN Jun-qi Key Laboratory of Soil Water Conservation & Desertification Combating of Ministry of Education School of Soil Water Conservation Beijing Forestry University PRChina Beijing Forestry Parks |
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| Institution: | HU Shu-ping1,YU Xin-xiao1,WANG Xiao-ping2,QIN Yong-sheng2,CHEN Jun-qi2.1 Key Laboratory of Soil , Water Conservation & Desertification Combating of Ministry of Education,School of Soil , Water Conservation,Beijing Forestry University,100083,P.R.China,2 Beijing Forestry , Parks Department of International Cooperation,100029,P.R.China. |
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| Abstract: | Vegetation types of protection forest system are influenced by multi-factors,and with complex nonlinear characteristics.Taking the idea of nature-approximating forestry,this paper established an optimization model of vegetation types of protection forest system using LM artificial neural network.The training and forecast test of the model was conducted by data of typical basin.Result indiates that the model constructed by Matlab is convenient and fast,with quick convergence rate and high forecast precision.... |
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| Keywords: | vegetation type optimization artificial neural network LM algorithm |
| 本文献已被 CNKI 等数据库收录！ |
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