Introducing interval analysis in fuzzy cognitive map framework

Authors:
Elpiniki Papageorgiou;Chrysostomos Stylios;Peter Groumpos
Affiliations:
Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, Rion, Greece;Department of Communications, Informatics and Management, TEI of Epirus, Artas Epirus, Greece;Laboratory for Automation and Robotics, Department of Electrical and Computer Engineering, University of Patras, Rion, Greece
Venue:
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Year:
2006

Citing 6
Cited 0

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Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

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Modeling complex systems using fuzzy cognitive maps

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Abstract

Fuzzy Cognitive Maps (FCMs) is a graphical model for causal knowledge representation. FCMs consist of nodes-concepts and weighted edges that connect the concepts and represent the cause and effect relationships among them. FCMs are used in complex problems involving causal relationships, which often include feedback, and where qualitative rather than quantitative measures of influences are available. They have used for decision support to determine a final state given a qualitative initial knowledge for nodes and weighted edges. A first study on introducing Interval analysis in the FCM framework has been attempted and it is presented in this work. Here a new structure for FCM is proposed with interval weights and a new method for processing interval data input for FCMs is proposed.