摘要
对农业机械化项目的可实施性以及实施效果进行科学、合理、正确的评价,可为其进一步发展和长期规划提供有效的决策依据。由于其地域环境、发展阶段、产业结构的不同存在着诸多模糊性和不确定性,建立多层次多指标的模糊综合评价模型进行模糊评判是较为常用的评判方法。但现有的模糊隶属度转换算法存在着目标分类不明确和出现冗余数值的问题,采用基于熵的数据挖掘方法,通过定义指标区分权,清除对目标分类不起作用的冗余数值,实现正确的隶属度转换并用于农业机械化项目绩效模糊综合评价中,使评价结果更具真实性和可靠性。
It is an important basis for the national economic development,and it＇s an important symbol of social progress in rural areas.Scientific,reasonable and correct evaluation on the enforceability and its effect of agricultural mechanization project（AMP）,can provide an effective decision for the further development and the long-term planning of agriculture.Because of different geographical environments,development and industrial structure,there are a lot of ambiguity and uncertainty in AMP.The common evaluation method is to establish fuzzy comprehensive evaluation model of multi-level and multi-index.But the existing membership degree transformation algorithm has the problems of indefinite target classification and redundant values.The data mining method based on entropy,through index distinguish right definition and elimination of the redundant values which has no effect on the target classification,can get the correct membership degree conversion to do AMP performance fuzzy comprehensive evaluation and the application examples have showed the whole process of conversion.
出处
《东北农业大学学报》
CAS
CSCD
北大核心
2012年第8期68-73,共6页
Journal of Northeast Agricultural University
基金
河北省自然科学基金资助项目(F2010001047)
关键词
农业机械化项目
模糊综合评价
隶属度转换
权矩阵
agricultural mechanization project（AMP）
fuzzy comprehensive evaluation
membership transformation
weight matrix
