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Learning FCM by chaotic simulated annealing

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  • Alizadeh, Somayeh
  • Ghazanfari, Mehdi

Abstract

Fuzzy cognitive map (FCM) is a directed graph, which shows the relations between essential components in complex systems. It is a very convenient, simple, and powerful tool, which is used in numerous areas of application. Experts who are familiar with the system components and their relations can generate a related FCM. There is a big gap when human experts cannot produce FCM or even there is no expert to produce the related FCM. Therefore, a new mechanism must be used to bridge this gap. In this paper, a novel learning method is proposed to construct FCM by using Chaotic simulated annealing (CSA). The proposed method not only is able to construct FCM graph topology but also is able to extract the weight of the edges from input historical data. The efficiency of the proposed method is shown via comparison of its results of some numerical examples with those of Simulated annealing (SA) method.

Suggested Citation

  • Alizadeh, Somayeh & Ghazanfari, Mehdi, 2009. "Learning FCM by chaotic simulated annealing," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1182-1190.
    • Handle: RePEc:eee:chsofr:v:41:y:2009:i:3:p:1182-1190
    DOI: 10.1016/j.chaos.2008.04.058
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    References listed on IDEAS

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    3. Michel G. Bougon, 1992. "Congregate Cognitive Maps: A Unified Dynamic Theory Of Organization And Strategy," Journal of Management Studies, Wiley Blackwell, vol. 29(3), pages 369-387, May.
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    Cited by:

    1. Mohsen Abbaspour Onari & Mustafa Jahangoshai Rezaee, 2022. "A fuzzy cognitive map based on Nash bargaining game for supplier selection problem: a case study on auto parts industry," Operational Research, Springer, vol. 22(3), pages 2133-2171, July.
    2. Liu, Lianggui & Zhang, Rui & Chen, Qiuxia, 2022. "High-performance global peak tracking technique for PV arrays subject to rapidly changing PSC," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    3. Sacchelli, S. & Fabbrizzi, S., 2015. "Minimisation of uncertainty in decision-making processes using optimised probabilistic Fuzzy Cognitive Maps: A case study for a rural sector," Socio-Economic Planning Sciences, Elsevier, vol. 52(C), pages 31-40.

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