ABSTRACT
Grey system theory and rough set theory are two dierent mathematical tools that are used to deal with uncertain or incomplete information, and yet they are relevant and complementary to a certain degree. ey both improve the generality of data presentation by reducing its accuracy. For example, grey system theory reduces the accuracy of data presentation through grey sequences generating, while rough set is through discrete data, which makes it possible to nd models from data that may be fuzzed by too many details, and neither of them needs a priori knowledge, such as probability distributions or membership, and so on. Rough set theory researches into the rough categories of nonoverlap and rough concepts, and concentrates on the indiscernibility between objects, while grey system theory researches into grey fuzzy sets, which have “clear extension, unclear connotation,” and concentrates on the uncertainty of poor information. e appropriate hybrid of the two theories can overcome the shortages of their denitions and applications and thus has more powerful functions.
