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基于多元统计和AHP的青藏高原牧区可持续发展评价模型与应用
引用本文:王晓鹏,曾永年,曹广超,丁生喜.基于多元统计和AHP的青藏高原牧区可持续发展评价模型与应用[J].系统工程理论与实践,2005,25(6):139-144.
作者姓名:王晓鹏  曾永年  曹广超  丁生喜
作者单位:(1) 青海师范大学数学与信息科学系;(2)中南大学信息物理工程学院;(3)青海师范大学地理与资源环境系;(4) 青海大学经济系
基金项目:国家自然科学基金(40071066),青海省社会科学规划基金(04sk0019)
摘    要:通过综合运用主成分分析和层次分析方法,建立系统综合评价的AHP变量加权主成分分析模型.在构建青藏高原牧区可持续发展评价指标体系的基础上,基于该模型方法对青海省内的30个牧区县给出发展现状的评价实例.最后,讨论了模型方法使用中的若干改进方法思路,以提高评价稳定性和效果.

关 键 词:青藏高原  可持续发展  主成分分析  层次分析法  加权变量    
文章编号:1000-6788(2005)06-0139-06
修稿时间:2004年7月9日

Evaluating Model and Application to Sustainable Development for Pastoral Areas on Qinghai-Tibetan Plateau Based on Multivariate Statistical Analysis and AHP
WANG Xiao-peng,ZENG Yong-nian,CAO Guang-chao,DING Sheng-xi.Evaluating Model and Application to Sustainable Development for Pastoral Areas on Qinghai-Tibetan Plateau Based on Multivariate Statistical Analysis and AHP[J].Systems Engineering —Theory & Practice,2005,25(6):139-144.
Authors:WANG Xiao-peng  ZENG Yong-nian  CAO Guang-chao  DING Sheng-xi
Institution:(1)Department of Mathematics and Information Sciences,Qingghai normal university;(2)School of Info-Physics and Geomatics Engineering,Central South University;(3)Department of Resource and Environmental Sciences,QingHai normal university;(4).Department of Economics, Qinghai University
Abstract:Using the methods of Principal Component Analysis (PCA) and the Analytical Hierarchy Process(AHP), the paper proposes the synthetic evaluating model of PCA with weighted variables by AHP. Establishing evaluation indicators system of sustainable development for pastoral areas on Qinghai-Tibetan Plateau, the authors also give the evaluating example of 30 counties in Qinghai based on this model, in which we make quantitative evaluation and qualitative analysis of sustainable development status for those counties. Furthermore,the paper discusses some refinement measure in order to improve stability and effectiveness for this model in application.
Keywords:Qinghai-Tibetan Plateau  sustainable development  Principal Component Analysis  Analytical Hierarchy Process  weighted variables
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