Abstract
In this paper, we present computational rule inferences to tackle the rate of aggregative risk in fuzzy circumstances. Based on the maximum membership grade principle, we apply the signed distance to defuzzify which is better than by the centroid. The proposed fuzzy assessment method is easier, closer to evaluator real thinking and more useful than the ones which have presented before. © 2009 Springer Berlin Heidelberg.
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CITATION STYLE
Lee, H. M., & Lin, L. (2009). A fuzzy risk assessment in software development defuzzified by signed distance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5712 LNAI, pp. 195–202). https://doi.org/10.1007/978-3-642-04592-9_25
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