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
Main Authors: Andreou, A.S., Mateou, N.H., Zombanakis, G.A.
Format: Conference Proceeding
Language:English
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.
DOI:10.1109/ICTTA.2004.1307788