基于CW算子的组合评价信息集结方法及其稳定性分析
[Abstract]:At present, the research on the comprehensive evaluation method has achieved fruitful results. However, due to the diversity of the analytical perspective, the difference of the principle of the method, the subjectivity of man-made judgment and so on, the conclusions obtained by different methods are often different. In order to solve the problem of inconsistent evaluation conclusion, combinatorial evaluation method has become the focus of scholars. Based on this, the existing research on combinatorial evaluation methods is carefully combed in order to lay a foundation for the development of new combinatorial evaluation methods. Then, in view of the particularity of the combinatorial evaluation method and the two-dimensional and uneven distribution of the evaluation data, this paper proposes an information aggregation method which is specially used to solve the combinatorial evaluation problem, that is, the combinatorial (CW) operator. This method can take into account the homogeneity and heterogeneity of a single method at the same time, and weighted twice on the basis of systematic cluster grouping, which is helpful to draw a more robust evaluation conclusion. Secondly, in order to further verify the effectiveness of the method in practice, it is applied to the performance evaluation of 16 listed commercial banks in 2015. The simple linear weighting method, grey correlation method, entropy method, ideal point method and improved ideal point method are applied to the performance evaluation of 16 listed commercial banks in 2015. Fuzzy comprehensive evaluation method and principal component analysis method are combined to get the corresponding conclusion. Finally, from the two perspectives of the number of evaluation methods and the number of evaluated objects, the stability analysis of the combinatorial evaluation method based on CW operator is carried out. Compared with the average method, the results show that the combination evaluation method based on CW operator is more stable than the average method.
【学位授予单位】:南昌大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:F224;F832.33
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