Optimization in genetically evolved fuzzy cognitive maps supporting decision-making: the limit cycle case

This paper proposes an extension of genetically evolved fuzzy cognitive maps (GEFCMs) aiming at increasing their reliability by overcoming its weakness appearing in cases of a limit cycle behavior. FCMs use notions borrowed from artificial intelligence and neural networks to combine concepts and cau...

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Bibliographic Details
Published inProceedings. 2004 International Conference on Information and Communication Technologies: From Theory to Applications, 2004 pp. 377 - 378
Main Authors Andreou, A.S., Mateou, N.H., Zombanakis, G.A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2004
Subjects
Artificial intelligence
Artificial neural networks
Computer aided software engineering
Computer science
Decision making
Equations
Fuzzy cognitive maps
Genetic algorithms
Limit-cycles
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Summary:This paper proposes an extension of genetically evolved fuzzy cognitive maps (GEFCMs) aiming at increasing their reliability by overcoming its weakness appearing in cases of a limit cycle behavior. FCMs use notions borrowed from artificial intelligence and neural networks to combine concepts and causal relationships, aimed at creating dynamic models that describe a given cognitive setting. The activation level of the nodes participating in an FCM model can be calculated using specific updating equations in a series of iterations.
ISBN:9780780384828
0780384822
DOI:10.1109/ICTTA.2004.1307788