In the comprehensive evaluation,the allocation of weights affects directly the accuracy and scientificity of evaluation results.Based on fuzzy entropy of the rough sets,a new method of determining weight in comprehensive evaluation is put forward in this paper,expressed effectively the different indicators to the different appraisal schemes have different information capacity,therefore each indicator to the different appraisal schemes should be given different weights,making the weight coefficient is easy to explain,and more objective and accurate.City facility level of North China,Northeast,and East China is analyzed by TOPSIS method,obtaining comprehensive place for city facility level of the three regions,the advantages and disadvantages of the three regions for city facility construction are discovered,and providing some suggestions for city facility construction of the three regions.

The application of RS&SVR method,which is support vector machine regression(SVR) based on attribute reduction algorithm of rough sets,on forecast of China's power supply is dealt with in this paper.According to historical data of power output and its influencing factors,a decision table is built up,and discretization of continuous attributes in the table is done by means of dynamic layer cluster.Using the attribute reduction algorithm to eliminate some redundant attributes from the table,the kernel factors are determined.Taking these kernel factors as the attributes of both training and testing samples,the power supply forecasting is conducted.Five-year forecasting results show that,compared with SVR which chooses attributes of input vectors in light of experience,the method of RS&SVR could make use of less but cardinal predictors' information,and the forecasting accuracy is improved.

The construction method of background value has an important influence on the precision and adaptability of the non-equidistant GM(1,1) model.Through the analysis of the background value in non-equidistant GM(1,1) model,a new method is proposed which reconstructs the background value in the model based on Newton interpolation and Newton-Cores formula and Gauss-Legendre formula of numerical integration respectively.This new formula of background value is suitable for both non-equidistant GM(1,1) model and equidistant GM(1,1) model.The application scope of GM(1,1) is enlarged,and the results of data simulation prove the effectiveness and superiority of the new model.