基于离差函数和联合熵的组合赋权方法

Combination Weighting Approach Based on Deviation Function and Joint-Entropy

针对多指标评价权重确定问题,提出了基于最小离差和最大广义联合熵的组合赋权方法。该方法一方面综合考虑了各评价方法从不同角度所确定的权向量,使确定的理想组合评价权向量与所有其他方法的评价权向量之间的总体偏差为最小;另一方面尽量消除组合赋权的不稳定性,使各方法各指标权数赋予平衡因子后广义的联合熵最大,使得全局的不确定性最小、最为合理,由此建立了组合权系数优化模型。最后通过实例说明了此方法的合理性。

From assigning the weights of multiple-index correctly, a new method based on minimal deviations and maximal joint entropy is proposed, in which the weight vectors at different point of views determined by several evaluation method was considered and the whole-warp between the ideal combination of the estimated weight vectors and the combination determined by other methods became minimal. Other hand, the joint-entropy at the multi-indicator vectors the most by choosing the balancing element and the uncertainty at the overall situation was the least in this method. Thus, the op- timization model about the weight vectors combination was constructed. An example is also given to demonstrate the precision and reliability and validity of the method.