Abstract
The scientific background that grey systems theory comes into being, the astonishing progress that grey systems theory has made in the world of learning and its wide-ranging applications in the entire spectrum of science, and the characteristics of unascertained systems include incomplete information and inaccuracies in data are presented in this paper. The scientific principle of simplicity and how precise models suffer from inaccuracies are also shown. We compared grey systems with other kinds of uncertainty models such as stochastic probability, rough set theory, and fuzzy mathematics. Finally, the elementary concepts and fundamental principles of grey systems, and main components of grey systems theory are introduced briefly.
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