2-Tuple and Rough Set Based Reduction Model for Multi-sensory Evaluation Indicators  

作　　者：夏雅琴 周洪雷 朱如鹏 

机构地区：[1]Key Laboratory of Precision and Micro-Manufacturing Technology,College of Mechanical and Electrical Engineering,Nanjing University of Aeronautics and Astronautics [2]Fashion Institute,Donghua University

出　　处：《Journal of Donghua University(English Edition)》2014年第1期50-56,共7页东华大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.50775108);Priority Academic Program Development of Jiangsu Higher Education Institutions,China(PAPD)

摘　　要：In order to lessen adverse influences of excessive evaluative indicators of the initial set in multi-sensory evaluation,a2-tuple and rough set based reduction model is built to simplify the initial set of evaluative indicators. In the model,a great variety of descriptive forms of the multi-sensory evaluation are also taken into consideration. As a result,the method proves effective in reducing redundant indexes and minimizing index overlaps without compromising the integrity of the evaluation system. By applying the model in a multi-sensory evaluation involving community public information service facilities,the research shows that the results are satisfactory when using genetic algorithm optimized BP neural network as a calculation tool. It shows that using the reduced and simplified set of indicators has a better predication performance than the initial set,and 2-tuple and rough set based model offers an efficient way to reduce indicator redundancy and improves prediction capability of the evaluation model.In order to lessen adverse influences of excessive evaluative indicators of the initial set in multi-sensory evaluation, a 2.tuple and rough set based reduction model is built to simplify the initial set of evaluative indicators. In the model, a great variety of descriptive forms of the multi-sensory evaluation are also taken into consideration. As a result, the method proves effective in reducing redundant indexes and minimizing index overlaps without compromising the integrity of the evaluation system. By applying the model in a multi-sensory evaluation involving community public information service facilities, the research shows that the results are satisfactory when using genetic algorithm optimized BP neural network as a calculation tool. It shows that using the reduced and simplified set of indicators has a better predication performance than the initial set, and 2-tuple and rough set based model offers an efficient way to reduce indicator redundancy and improves prediction capability of the evaluation model.

关 键 词：indicator reduction 2-tuple rough set multi-sensory evaluation 

分 类 号：TB47[一般工业技术—工业设计]