{"title":"A kind of synthetic evaluation method based on the attribute computing network","authors":"Xiaolin Xu, Guanglin Xu, Jia-li Feng","doi":"10.1109/GRC.2009.5255044","DOIUrl":null,"url":null,"abstract":"Based on input and output relationship of Qualitative Mapping(QM), the attribute computing network model has been created. It brings forward a kind of computing method using input to adjust qualitative benchmark of attribute network, which makes it possible to achieve pattern recognition. Now the new attribute computing network model combined pattern recognition with synthetic evaluation is established. Firstly qualitative benchmarks of indexes are gotten by boundary study, and then by way of marking, preference for indexes is obtained, and lastly a set of satisfactory degrees for indexes is computed and outputted in descending sequence which ameliorates the effect of old satisfactory degree. Finally the simulation experiment is carried out to validate the theoretical model.","PeriodicalId":388774,"journal":{"name":"2009 IEEE International Conference on Granular Computing","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Granular Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRC.2009.5255044","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
Based on input and output relationship of Qualitative Mapping(QM), the attribute computing network model has been created. It brings forward a kind of computing method using input to adjust qualitative benchmark of attribute network, which makes it possible to achieve pattern recognition. Now the new attribute computing network model combined pattern recognition with synthetic evaluation is established. Firstly qualitative benchmarks of indexes are gotten by boundary study, and then by way of marking, preference for indexes is obtained, and lastly a set of satisfactory degrees for indexes is computed and outputted in descending sequence which ameliorates the effect of old satisfactory degree. Finally the simulation experiment is carried out to validate the theoretical model.