{"title":"Learning FCM by Tabu Search","authors":"Somayeh Alizadeh, Mehdi Ghazanfari, Mostafa Jafari, Salman Hooshmand","volume":9,"journal":"International Journal of Computer and Information Engineering","pagesStart":2784,"pagesEnd":2792,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/12453","abstract":"
Fuzzy Cognitive Maps (FCMs) is a causal graph, which shows the relations between essential components in complex systems. Experts who are familiar with the system components and their relations can generate a related FCM. There is a big gap when human experts cannot produce FCM or even there is no expert to produce the related FCM. Therefore, a new mechanism must be used to bridge this gap. In this paper, a novel learning method is proposed to construct causal graph based on historical data and by using metaheuristic such Tabu Search (TS). The efficiency of the proposed method is shown via comparison of its results of some numerical examples with those of some other methods.<\/p>\r\n","references":"[1] R.Axelrod, Structure of Decision: The Cognitive Maps of Political\r\nElites, Princeton University Press, Princeton, NJ, 1976.\r\n[2] M.A. Styblinski, B.D. Meyer, Signal flow graphs versus fuzzy\r\ncognitive maps in application to qualitative circuit analysis, Internet. J.\r\nMan Mach. 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