摘要
基于熵的最优化原理建立了一种新的企业危机预警模型.首先利用最小判别熵选取企业危机预警特征值;然后提出一种新的聚类算法——极大熵聚类算法,并对预测结果进行分类,判断企业的危机状态.该算法是硬C-均值算法的发展和推广.通过实例分析表明,该模型有效、可行,为企业危机预警提供了一条新的途径.
Based on entropy optimal theory, a new model for early-warning of crisis is established. Firstly, minimum J-divergence entropy is applied to feature extraction. Then the calculating result is classified to judge state of enterprise with a new clustering algorithm, maximum entropy clustering algorithm, which is a development and extension of hard C-means. Finally, an example in early-warning of enterprise crisis is given to validate the model. The results show the feasibility and validity of the model. The research work supplies a new way for early-warning of enterprise crisis.
出处
《控制与决策》
EI
CSCD
北大核心
2009年第1期113-117,121,共6页
Control and Decision
基金
国家自然科学基金项目(70372011)
关键词
企业危机预警
最小判别熵
特征提取
熵聚类算法
Early-warning of enterprise crisis
Minimum J-divergence entropy
Feature extraction
Entropy clustering algorithm
