Abstract
This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems; they combine aspects of fuzzy logic, neural networks, semantic networks, expert systems, and nonlinear dynamical systems. They are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. First, the description and the methodology that this theory suggests is examined, later some ideas for using this approach in the control process area are discussed. An inspired on particle swarm optimization learning method for this technique is proposed, and then, the implementation of a tool based on Fuzzy Cognitive Maps is described. The application of this theory might contribute to the progress of more intelligent and independent systems. Fuzzy Cognitive Maps have been fruitfully used in decision making and simulation of complex situation and analysis. At the end, a case study about Travel Behavior is analyzed and results are assessed.
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León, M., Nápoles, G., Rodriguez, C., García, M.M., Bello, R., Vanhoof, K. (2011). A Fuzzy Cognitive Maps Modeling, Learning and Simulation Framework for Studying Complex System. In: Ferrández, J.M., Álvarez Sánchez, J.R., de la Paz, F., Toledo, F.J. (eds) New Challenges on Bioinspired Applications. IWINAC 2011. Lecture Notes in Computer Science, vol 6687. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21326-7_27
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DOI: https://doi.org/10.1007/978-3-642-21326-7_27
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