计算机工程 ›› 2012, Vol. 38 ›› Issue (9): 158-161.doi: 10.3969/j.issn.1000-3428.2012.09.048

• 人工智能及识别技术 • 上一篇    下一篇

基于粗糙集和CBR的救灾口粮需求预测

吴雪莲1,2,孙丙宇1,2,李文波2,张 洁2   

  1. (1. 中国科学技术大学自动化系,合肥 230026;2. 中国科学院合肥智能机械研究所,合肥 230031)
  • 收稿日期:2011-07-04 出版日期:2012-05-05 发布日期:2012-05-05
  • 作者简介:吴雪莲(1975-),女,硕士研究生,主研方向:模式识别,人工智能;孙丙宇,研究员、博士;李文波,副研究员、博士; 张 洁,硕士研究生
  • 基金项目:
    国家科技支撑计划基金资助项目(2008BAK49B05);国家自然科学基金资助项目(41101516, 91024008)

Relief Food Demand Forecast Based on Rough Set and Case-based Reasoning

WU Xue-lian 1, 2, SUN Bing-yu 1, 2, LI Wen-bo 2, ZHANG Jie 2   

  1. (1. Department of Automation, University of Science and Technology of China, Hefei 230026, China; 2. Institute of Intelligent Machine, Chinese Academy of Sciences, Hefei 230031, China)
  • Received:2011-07-04 Online:2012-05-05 Published:2012-05-05

摘要: 救灾口粮预测所采用的方法多以专家经验判断为主,具有较大的随机性。为此,从灾害案例的特点出发,针对案例推理时存在效率低下和权重确定差异性较大的问题,结合粗糙集处理不确定知识的优点和案例推理的特点,提出一种方法,实现灾害应急救灾口粮需求预测,并通过洪涝灾害实例进行分析。结果表明该方法有利于减少主观影响,提高需求预测的准确率和效率。

关键词: 粗糙集, 案例推理, 救灾口粮, 属性约简, 属性重要度, 需求预测

Abstract: The methods for forecasting relief food, which are based on the experts and the prediction results, are influenced by the experience of the experts. Considering the advantage of rough set in dealing with the uncertain knowledge, this paper proposes a novel method that combines rough set with Case-based Reasoning(CBR) for forecasting relief food demand. This method utilizes discernibility matrix and attributes significance to reduce case attributes and get attribute weights. Experimental results prove that the proposed method can reduce the subjective effects and improve the predication accuracy.

Key words: rough set, Case-based Reasoning(CBR), relief food, attribute reduction, attribute significance, demand forecast

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