计算机应用 ›› 2014, Vol. 34 ›› Issue (2): 519-522.

• 人工智能 • 上一篇    下一篇

基于集对分析与马尔可夫链的生态安全动态评估

石欣,张涛,雷璐宁   

  1. 重庆大学 自动化学院,重庆400030
  • 收稿日期:2013-08-19 修回日期:2013-10-21 出版日期:2014-02-01 发布日期:2014-03-01
  • 通讯作者: 石欣
  • 作者简介:石欣(1978-),男,重庆人,副教授,主要研究方向:智能感知、控制与决策;张涛(1990-),男,四川苍溪人,硕士研究生,主要研究方向:智能信息处理、机器学习;雷璐宁(1986-),女,重庆人,博士研究生,主要研究方向:智能控制与智能管理、机器学习。
  • 基金资助:
    国家科技支撑计划项目

Dynamic evaluation of ecological security based on set pair analysis and Markov chain

SHI Xin,ZHANG Tao,LEI Luning   

  1. College of Automation, Chongqing University, Chongqing 400030, China
  • Received:2013-08-19 Revised:2013-10-21 Online:2014-02-01 Published:2014-03-01
  • Contact: SHI Xin

摘要: 针对生态安全中诸多不确定性因素和动态变化的特点,将集对分析(SPA)理论和马尔可夫链(Markov Chain)相结合用于生态安全的动态评估中,提出一种集状态评价和趋势分析的生态安全动态评价预测方法。采用集对分析的联系度对不确定性知识进行表示,用联系数对生态安全的等级进行划分,构建系统综合评价模型,通过对生态安全发展趋势进行分析,运用Markov Chain的遍历性和蒙特卡洛(Monte Carlo)方法的统计特性,预测系统下一时刻的生态安全状态。该方法运用了有限的评估数据与历史数据,动态地评估了生态安全的发展变化,为安全管理提供了理论依据。

关键词: 生态安全,动态评估,集对分析,马尔可夫链,蒙特卡洛方法

Abstract: Concerning the situation that the ecological security has a lot of uncertaint factors and is dynamic, the Set Pair Analysis (SPA) theory and Markov chain were combined for dynamic assessment of ecological security, and a dynamic assessment method of ecological security was proposed combining the state assessment and trend analysis. The method adopted connection degree of SPA to represent uncertain knowledge, used the connection number of SPA to classify ecological security level, and built the comprehensive evaluation model of the system. Through the analysis of the development trend of ecological security, using the ergodicity of Markov chain and statistical quality of Monte Carlo method, the state of ecological security in the next moment could be predicted. This method uses limited assessment data and historical data, dynamically assesses the development and changes of ecological security, and provides a theoretical basis for the safety management.

Key words: Ecological Security, Dynamic Assessment, Set Pair Analysis (SPA), Markov Chain (MC), Monte Carlo method

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