Fuzzy Cognitive Maps for Modeling Complex Systems

This paper presents Fuzzy Cognitive Maps as an approach in modeling the behavior and operation of complex systems. This technique is the fusion of the advances of the fuzzy logic and cognitive maps theories, they are fuzzy weighted directed graphs with feedback that create models that emulate the behavior of complex decision processes using fuzzy causal relations. There are some applications in diverse domains (manage, multiagent systems, etc.) and novel works (dynamical characteristics, learning procedures, etc.) to improve the performance of these systems. First the description and the methodology that this theory suggests is examined, also some ideas for using this approach in the control process area, and then the implementation of a tool based on Fuzzy Cognitive Maps is described. The application of this theory in the field of control and systems might contribute to the progress of more intelligent and independent control systems. Fuzzy Cognitive Maps have been fruitfully used in decision making and simulation of complex situation and analysis.

[1]  Michael Reinfrank,et al.  An introduction to fuzzy control (2nd ed.) , 1996 .

[2]  Robert Fullér,et al.  Introduction to neuro-fuzzy systems , 1999, Advances in soft computing.

[3]  Dimitrios E. Koulouriotis,et al.  EFFICIENTLY MODELING AND CONTROLLING COMPLEX DYNAMIC SYSTEMS USING EVOLUTIONARY FUZZY COGNITIVE MAPS (INVITED PAPER) , 2003 .

[4]  José Manuel Gutiérrez,et al.  Expert Systems and Probabiistic Network Models , 1996 .

[5]  Marcelo Simoes Introduction to Fuzzy Control , 2003 .

[6]  Bart Kosko,et al.  Fuzzy Cognitive Maps , 1986, Int. J. Man Mach. Stud..

[7]  Dr. Hans Hellendoorn,et al.  An Introduction to Fuzzy Control , 1996, Springer Berlin Heidelberg.

[8]  Jose Aguilar,et al.  A DYNAMIC FUZZY-COGNITIVE-MAP APPROACH BASED ON RANDOM NEURAL NETWORKS , 2003 .

[9]  Enrique F. Castillo,et al.  Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.

[10]  Konstantinos G. Margaritis,et al.  A New Balance Degree for Fuzzy Cognitive Maps , 2022 .

[11]  Jacek M. Leski,et al.  Fuzzy and Neuro-Fuzzy Intelligent Systems , 2000, Studies in Fuzziness and Soft Computing.

[12]  Bart Kosko,et al.  Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence , 1991 .

[13]  C. Carlsson,et al.  Adaptive Fuzzy Cognitive Maps for Hyperknowledge Representation in Strategy Formation Process , 1996 .

[14]  Stephen T. Mohr,et al.  Software Design For a Fuzzy Cognitive Map Modeling Tool , 1997 .

[15]  Xiaoou Li,et al.  Dynamic knowledge inference and learning under adaptive fuzzy Petri net framework , 2000, IEEE Trans. Syst. Man Cybern. Part C.

[16]  Norman S. Nise,et al.  Control Systems Engineering , 1991 .

[17]  Chrysostomos D. Stylios,et al.  Modeling complex systems using fuzzy cognitive maps , 2004, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.