Adaptive estimation of fuzzy cognitive maps with proven stability and parameter convergence
IEEE Transactions on Fuzzy Systems
Mining temporal medical data using adaptive fuzzy cognitive maps
HSI'09 Proceedings of the 2nd conference on Human System Interactions
Transformation of cognitive maps
IEEE Transactions on Fuzzy Systems
Fuzzy temporal constraints based fuzzy clustering algorithm for temporal dadaset
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
IEEE Transactions on Fuzzy Systems
A divide and conquer method for learning large Fuzzy Cognitive Maps
Fuzzy Sets and Systems
Structural damage detection using fuzzy cognitive maps and Hebbian learning
Applied Soft Computing
Bi-linear adaptive estimation of Fuzzy Cognitive Networks
Applied Soft Computing
A flexible nonlinear approach to represent cause-effect relationships in FCMs
Applied Soft Computing
From fuzzy cognitive maps to granular cognitive maps
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part I
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In this paper, we introduce a novel approach to time-series prediction realized both at the linguistic and numerical level. It exploits fuzzy cognitive maps (FCMs) along with a recently proposed learning method that takes advantage of real-coded genetic algorithms. FCMs are used for modeling and qualitative analysis of dynamic systems. Within the framework of FCMs, the systems are described by means of concepts and their mutual relationships. The proposed prediction method combines FCMs with granular, fuzzy-set-based model of inputs. One of their main advantages is an ability to carry out modeling and prediction at both numerical and linguistic levels. A comprehensive set of experiments has been carried out with two major goals in mind. One is to assess quality of the proposed architecture, the other to examine the influence of its parameters of the prediction technique on the quality of prediction. The obtained results, which are compared with other prediction techniques using fuzzy sets, demonstrate that the proposed architecture offers substantial accuracy expressed at both linguistic and numerical levels.