Fuzzy cognitive maps (FCMs) that are soft computing techniques, by combining fuzzy logic and neural network theory, have been known as a powerful tool for modeling complex systems. Utilization of different learning algorithms to overcome the weaknesses of this model, is one of the active area of science. In this paper, a new hybrid algorithm based on nonlinear Hebbian learning and real-coded genetic algorithm is introduced, which operate in an entangled way and by improving the characteristics of each of these two algorithms, can be applied in different decision-making models with high precision. The proposed model is implemented on a process control problem.
Mosavi, M. R., Mohseni, A., & Amirkhani, A. (2017). An entangled hybrid algorithm for training fuzzy cognitive maps. Electronics Industries, 8(2), 5-14.
MLA
M. R. Mosavi; Akram Mohseni; Abdollah Amirkhani. "An entangled hybrid algorithm for training fuzzy cognitive maps". Electronics Industries, 8, 2, 2017, 5-14.
HARVARD
Mosavi, M. R., Mohseni, A., Amirkhani, A. (2017). 'An entangled hybrid algorithm for training fuzzy cognitive maps', Electronics Industries, 8(2), pp. 5-14.
VANCOUVER
Mosavi, M. R., Mohseni, A., Amirkhani, A. An entangled hybrid algorithm for training fuzzy cognitive maps. Electronics Industries, 2017; 8(2): 5-14.
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