Online from: 2011
Subject Area: Information and Knowledge Management
|Title:||A brief introduction to grey systems theory|
|Author(s):||Sifeng Liu, (Institute for Grey Systems Studies, Nanjing University of Aeronautics and Astronautics, Nanjing, China), Jeffrey Forrest, (Mathematics Department of Slippery Rock University, Slippery Rock, Pennsylvania, USA), Yingjie Yang, (Centre for Computational Intelligence, De Montfort University, Leicester, UK)|
|Citation:||Sifeng Liu, Jeffrey Forrest, Yingjie Yang, (2012) "A brief introduction to grey systems theory", Grey Systems: Theory and Application, Vol. 2 Iss: 2, pp.89 - 104|
|Keywords:||Appearance, Development, Grey systems theory, Mathematics, Uncertain systems|
|Article type:||Research paper|
|DOI:||10.1108/20439371211260081 (Permanent URL)|
|Publisher:||Emerald Group Publishing Limited|
|Acknowledgements:||The relevant research is done in this paper are supported by the joint research project of both the Natural Science Foundation of China (NSFC, No. 71111130211)) and the Royal Society (RS) of UK, the Natural Science Foundation of China (Nos 90924022, 7097106 and 70901041), the major project and key project of Social Science Foundation of the China (No. 10zd&014, No. 08AJY024), the key project of Soft Science Foundation of China (2008GXS5D115), the Foundation for Doctoral Programs (200802870020) and the Foundation for Humanities and Social Sciences of the Chinese National Ministry of Education (No. 08JA630039). At the same time, the authors would like to acknowledge the partial support of the Science Foundation for the Excellent and Creative group in Science and Technology of Jiangsu Province (No. Y0553-091), the Foundation for Key Research Base of Philosophy and Social Science in colleges and universities of Jiangsu Province, and the Foundation for national outstanding teaching group of China (No. 10td128).|
Purpose – The purpose of this paper is to introduce the elementary concepts and fundamental principles of grey systems and the main components of grey systems theory. Also to discuss 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.
Design/methodology/approach – The characteristics of unascertained systems including incomplete information and inaccuracies in data are analysed and four uncertain theories: probability statistics, fuzzy mathematics, grey system and rough set theory are compared. The scientific principle of simplicity and how precise models suffer from inaccuracies are also shown.
Findings – The four uncertain theories, probability statistics, fuzzy mathematics, grey system and rough set theory are examined with different research objects, different basic sets, different methods and procedures, different data requirements, different emphasis, different objectives and different characteristics.
Practical implications – The scientific principle of simplicity and how precise models suffer from inaccuracies are shown. So, precise models are not necessarily an effective means to deal with complex matters, especially in the case that the available information is incomplete and the collected data inaccurate.
Originality/value – The elementary concepts and fundamental principles of grey systems and the main components of grey systems theory are introduced briefly. The reader is given a general picture of grey systems theory as a new method for studying problems where partial information is known, partial information is unknown; especially for uncertain systems with few data points and poor information.
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