An entangled hybrid algorithm for training fuzzy cognitive maps

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MOUSAVI S.M.R.; Mohseni Akram; AMIRKHANI ABDOLLAH

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Journal: ELECTRONIC INDUSTRIES Year:2017 | Volume:8 | Issue:2 Page(s): 5-14

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Title

An entangled hybrid algorithm for training fuzzy cognitive maps

Author(s)

MOUSAVI S.M.R. | Mohseni Akram | AMIRKHANI ABDOLLAH | Issue Writer Certificate

Keywords

Hybrid learning algorithmQ1

Genetic AlgorithmQ1

Fuzzy cognitive mapQ1

Nonlinear Hebbian learningQ1

Abstract

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.

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APA: Copy

MOUSAVI, S.M.R., Mohseni, Akram, & AMIRKHANI, ABDOLLAH. (2017). An entangled hybrid algorithm for training fuzzy cognitive maps. ELECTRONIC INDUSTRIES, 8(2 ), 5-14. SID. https://sid.ir/paper/229620/en

Vancouver: Copy

MOUSAVI S.M.R., Mohseni Akram, AMIRKHANI ABDOLLAH. An entangled hybrid algorithm for training fuzzy cognitive maps. ELECTRONIC INDUSTRIES[Internet]. 2017;8(2 ):5-14. Available from: https://sid.ir/paper/229620/en

IEEE: Copy

S.M.R. MOUSAVI, Akram Mohseni, and ABDOLLAH AMIRKHANI, “An entangled hybrid algorithm for training fuzzy cognitive maps,” ELECTRONIC INDUSTRIES, vol. 8, no. 2 , pp. 5–14, 2017, [Online]. Available: https://sid.ir/paper/229620/en

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