The Algorithm of Fully Fuzzy Cognitive Map Models

##plugins.themes.bootstrap3.article.main##

  •   Erastus O. Ogunti

  •   Oluwasegun A. Somefun

  •   Benedict T. Terkura

  •   Gideon E. Enoch

Abstract

Precision in the real world is covered by imprecision and arithmetic operations serve as the foundations of computation. Since the introduction of Fuzzy Cognitive mapping, the dynamic model used to establish the fuzzy cognitive map, used conventional arithmetic operations on asymmetric fuzzy sets.Therefore, for a cognitive map, to be completely fuzzy, it should incorporate the use of fuzzy arithmetic and fuzzy numbers in describing the concept nodes and the cause-effect lines defining its structure. It then can be stated that the necessary and sufficient condition for a cognitive map to be fully fuzzy is that its dynamic activity or operation, be achieved only through fuzzy mathematics.This paper presents an introductory analysis into the peculiar design of the fully fuzzy structure of the cognitive map.


Keywords: Algorithm, Cognitive Map, Fully Fuzzy, Dynamic Model

References

G. Słoń, ‘The Use of Fuzzy Numbers in the Process of Designing Relational Fuzzy Cognitive Maps’, in Artificial Intelligence and Soft Computing, vol. 7894, L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 376–387.

R. Alexrod, Ed., Structure of decision: The cognitive maps of political elites. Princeton University Press, 1976.

B. Kosko, ‘Fuzzy cognitive maps’, Int. J. Man-Mach. Stud., vol. 24, no. 1, pp. 65–75, 1986.

M. León, C. Rodriguez, M. M. García, R. Bello, and K. Vanhoof, ‘Fuzzy Cognitive Maps for Modeling Complex Systems’, in Advances in Artificial Intelligence, vol. 6437, G. Sidorov, A. Hernández Aguirre, and C. A. Reyes García, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 166–174.

E. I. Papageorgiou and J. L. Salmeron, ‘Methods and Algorithms for Fuzzy Cognitive Map-based Modeling’, in Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms, E. I. Papageorgiou, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 1–28.

L. A. Zadeh, ‘Fuzzy sets’, Inf. Control, vol. 8, no. 3, pp. 338–353, 1965.

L. A. Zadeh, ‘Calculus of Fuzzy Restrictions’, in Fuzzy Sets and their Applications to Cognitive and Decision Processes, L. A. Zadeh, K.-S. Fu, K. Tanaka, and M. Shimura, Eds. Academic Press, 1975, pp. 1–39.

G. Sloń and A. Yastrebov, ‘Designing and Training Relational Fuzzy Cognitive Maps’, in Fuzzy Cognitive Maps for Applied Sciences and Engineering: From Fundamentals to Extensions and Learning Algorithms, E. I. Papageorgiou, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014, pp. 145–157.

V. V. Borisov and A. S. Fedulov, ‘Generalized Rule-Based Fuzzy Cognitive Maps: Structure and Dynamics Model’, Berlin, Heidelberg, 2004, pp. 918–922.

G. Słoń and A. Yastrebov, ‘Optimization and Adaptation of Dynamic Models of Fuzzy Relational Cognitive Maps’, in Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, vol. 6743, S. O. Kuznetsov, D. Ślęzak, D. H. Hepting, and B. G. Mirkin, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 95–102.

G. Słoń, ‘Application of Models of Relational Fuzzy Cognitive Maps for Prediction of Work of Complex Systems’, in Artificial Intelligence and Soft Computing, 2014, pp. 307–318.

G. Słoń, ‘Application of Models of Relational Fuzzy Cognitive Maps for Prediction of Work of Complex Systems’, in Artificial Intelligence and Soft Computing, vol. 8467, L. Rutkowski, M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada, Eds. Cham: Springer International Publishing, 2014, pp. 307–318.

V. Borisov, A. Fedulov, and Y. Fedulov, ‘“Compatible” Fuzzy Cognitive Maps for Direct and Inverse Inference’, in Proceedings of the 18th International Conference on Computer Systems and Technologies - CompSysTech’17, Ruse, Bulgaria, 2017, pp. 20–27.

G. Słoń and A. Yastrebov, ‘Application of Fuzzy Relational Cognitive Maps in Intelligent Modeling the Technical Systems’, Poznan Univ. Technol. Acad. J., p. 8, 2012.

C. D. Stylios and P. P. Groumpos, ‘Modeling Complex Systems Using Fuzzy Cognitive Maps’, IEEE Trans. Syst. Man Cybern. - Part Syst. Hum., vol. 34, no. 1, pp. 155–162, Jan. 2004.

L. A. Zadeh, ‘Fuzzy algorithms’, Inf. Control, vol. 12, no. 2, pp. 94–102, 1968.

G. Slon and A. Yastrebov, ‘Remarks on the uncertainty expansion problem in calculations of models of relational fuzzy cognitive maps’, in 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples, Italy, 2017, pp. 1–8.

Alexander S. Fedulov, ‘Fuzzy relational cognitive maps’, J. Comput. Syst. Sci. Int., vol. 44, no. 1, pp. 112–124, 2005.

A. S. Fedulov and V. Borisov V., ‘Models of System Dynamics Based on Fuzzy Relational Cognitive Maps’, Syst. Control Commun. Secur., no. 1, pp. 66–80, 2016.

Downloads

Download data is not yet available.

##plugins.themes.bootstrap3.article.details##

How to Cite
[1]
Ogunti, E., Somefun, O., Terkura, B. and Enoch, G. 2019. The Algorithm of Fully Fuzzy Cognitive Map Models. European Journal of Engineering and Technology Research. 4, 6 (Jun. 2019), 145-154. DOI:https://doi.org/10.24018/ejers.2019.4.6.1325.