摘要
为了克服传统交合分析法(CA)存在的总体效用模型可能会失效及没有较好反映评价问题内在复杂作用关系和没有考虑到专家主观判断不准确性的缺陷,本文借鉴从定性到定量综合集成的复杂系统分析方法论,应用模糊神经网络技术,提出一种基于模糊神经网络的交合分析改进方法。该方法能够比较有效地近似反映出复杂系统蕴含的、难以为专家识别的内在复杂作用机理,因而是一种能够适应各种系统评价问题的一般性、普适性方法。数值验证的结果表明,应用基于模糊神经网络的交合分析改进方法得出的待评价对象排序结果明显好于传统CA法。这说明该方法不但是科学的,而且是比传统CA法更为有效的。
Such drawbacks of CA as probably being invalid under some conditions, having not enough ability to well reflect inherent relations of complex systems, and not considering the inaccurate characteristic of evaluations given by valuators, are pointed out. To overcome the three drawbacks, according to the thought of the meta-synthesis from qualitative analysis to quantitative analysis of complex system theory, and based on the theory of the technique of fuzzy neural network, the improved CA based on FNN is presented. The distinguished advantages of the presented approach lie in that it can well capture inherent relations of complex systems, and thus it is a general approach. The results of numerical demonstration analysis show that the rank of evaluated objects got by the developed approach is much closer to the real, and proves to be more reasonable than the classical CA.
出处
《中国管理科学》
CSSCI
2008年第1期117-124,共8页
Chinese Journal of Management Science
基金
国家自然科学基金项目(70471015)
吉林大学创新基金项目(2004g11)
关键词
交合分析法
模糊神经网络
语言型信息
系统机理
conjoint analysis
fuzzy neural network
linguistic information
system mechanism
