Algorithm Of Membership Degree Transformation Research Articles

Fuzzy Evaluation on Food Safety Based on Improved Membership Degree Transformation Algorithm

It needs a scientific and complete index system for a comprehensive evaluation on the safety of foods in some region or some country. And the use of advanced evaluation technologies is also an important work of food safety evaluation. The paper eliminated the redundant data in index membership for object classification by defining distinguishable weight and extracted valid values to compute object membership. The improved algorithm of membership degree transformation includes three calculation steps which can be summarized as “effective, comparison and composition”, which is denoted as M (1,2,3). The paper applied the new algorithm in fuzzy evaluation on the safety of foods. Evaluation results show the validity of the improved model and the model can achieve the dynamic evaluation of the safety of foods in some region or some country.

Fuzzy Evaluation on Network Security Based on the New Algorithm of Membership Degree Transformation—M(1,2,3)

Network security has close relationships with the physical environment of information carriers, information transmission, information storage, information management and so on, and these relations have much ambiguity. Therefore, it is reasonable and scientific to apply fuzzy comprehensive evaluation method for network security fuzzy evaluation. The core of network security fuzzy evaluation is membership degree transformation calculation. But the transformation methods should be discussed, because redundant data in index membership degree is also used to compute object membership degree, which is not useful for object classification. The new algorithm is: using data mining technology based on entropy to mine knowledge information about object classification hidden in every index, affirm the relationship of object classification and index membership, eliminate the redundant data in index membership for object classification by defining distinguishable weight and extract valid values to compute object membership. The new algorithm of membership degree transformation includes three calculation steps which can be summarized as “effective, comparison and composition”, which is denoted as M(1,2,3). The paper applied the new algorithm in network security fuzzy evaluation.