Abstract
This paper examines fuzzy cognitive map (FCM) theory and its use in supervisory control systems. An FCM is a graph used to depict cause and effect between concepts that stand for the states and variables of the system. An FCM represents the whole system in a symbolic manner, just as humans have stored the operation of the system in their brains, thus it is possible to help man's intention for more intelligent and autonomous systems. FCM representation, construction and a mathematical model are examined; a generic system is proposed and the implementation of FCM in a process control problem is illustrated and a model for supervisors of manufacturing systems is discussed. Although an FCM seems to be a simple model of system behaviour, it appears to be a powerful and effective tool describing the behaviour of a system and representing the accumulated knowledge of a system.
Similar content being viewed by others
References
Axelrod, R. (1976) Structure of Decision, Princeton University Press, NJ.
Craiger, J. P. and Coovert, M. D. (1994) Modeling dynamic social and psychological processes with fuzzy cognitive maps. Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 26 June-2 July, Orlando, FL, Vol 3,1873–1877.
Craiger, J. P., Goodman, D. F., Weiss, R. J., and Butler, A. B. (1996) Modeling organizational behavior with fuzzy cognitive maps. International Journal of Computational Intelligence and Organizations, 1, 120–123.
Dickerson, J. A. and Kosko, B. (1994) Fuzzy virtual worlds. Artificial Intelligence Expert, No. 7, pp. 25–31.
Gotoh, K., Murakami, J., Yamaguchi, T. and Yamanaka, Y. (1989) Application of fuzzy cognitive maps to supporting for plant control, in Proceedings of the SICE Joint Symposium of Fifteenth Systems Symposium and Tenth Knowledge Engineering Symposium, 19-21 October, Sapporo, pp. 99–104.
Kosko, B. (1986) Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24, 65–75.
Kosko, B. (1992) Neural Networks and Fuzzy Systems, Prentice-Hall, Englewood Cliffs, NJ, pp. 152–158.
Pelaez, C. E. and Bowles, J. B. (1995) Applying fuzzy cognitive maps knowledge representation to failure modes effects IEEE analysis, in Proceedings of the Annual Reliability and Maintainability Symposium, 17-19 January, Washington, DC, pp. 450–455.
Pelaez, C. E. and Bowles, J. B. (1996) Using fuzzy cognitive maps as a system model for failure models and effects analysis. Information Sciences, 88, 177–199.
Styblinski, M. A. and Meyer, B. D. (1991) Signal flow graphs versus fuzzy cognitive maps in application to qualitative circuit analysis. International Journal of Man-Machine Studies, 35, 175–186.
Stylios, C. D., Georgopoulos, V. C. and Groumpos, P. P. (1997) Introducing the theory of fuzzy cognitive maps in distributed systems, in Proceedings of the Twelfth IEEE International Symposium on Intelligent Control, 16-18 July, Istanbul, Turkey, pp. 55–60.
Taber, R. (1991) Knowledge processing with fuzzy cognitive maps. Expert Systems with Applications, 2, 83–87.
Taber, R. (1994) Fuzzy cognitive maps model social systems. Artificial Intelligence Expert, 9, 18–23.
Zhang, W. R. and Chen, S. S. (1988) A logical architecture for cognitive maps, in Proceedings of the Second IEEE International Conference on Neural Networks, 24-27 July, San Diego, CA, Vol. 2, pp. 381–388.
Zhang, W. R., Chen, S. S., and Besdek, J. C. (1989) Pool 2: a generic system for cognitive map development and decision analysis. IEEE Transactions on Systems, Man and Cybernetics, 19, 31–39.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Stylios, C.D., Groumpos, P.P. The challenge of modelling supervisory systems using fuzzy cognitive maps. Journal of Intelligent Manufacturing 9, 339–345 (1998). https://doi.org/10.1023/A:1008978809938
Issue Date:
DOI: https://doi.org/10.1023/A:1008978809938