International Journal of Man-Machine Studies
Fuzzy engineering
Adaptive Random Fuzzy Cognitive Maps
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links
International Journal of Human-Computer Studies
Agents-based design for fault management systems in industrial processes
Computers in Industry
Mining temporal medical data using adaptive fuzzy cognitive maps
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Genetic learning of fuzzy cognitive maps
Fuzzy Sets and Systems
Adaptive Fuzzy Cognitive Maps for Identification of Cause and Effect
CICSYN '11 Proceedings of the 2011 Third International Conference on Computational Intelligence, Communication Systems and Networks
A multiagent model for intelligent distributed control systems
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
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We study in this work the problem of adaptation on cognitive maps (CMs). We review different approaches of adaptation for CM, based on the idea that the causal relationships of the CM change during their phase of execution (runtime). Particularly, we study three dynamic causal relationships: the first one where the relationships between the concepts are defined as fuzzy rules, and the concepts and the relationship are fuzzy variables; the second one where mathematical models that describe the real system are used to define the causal relationships; and finally, in the last one the causal relationships are defined by generic logic rules based on the state of the concepts of the map. Each one can be used to model different types of systems, because each one exploits specific characteristics of the modeled system. These approaches are tested in different problems, giving very good results, and demonstrating that the utilization of CM as dynamic models is reliable and good.