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Open Access

Unsupervised Dynamic Fuzzy Cognitive Map

Boyuan Liu, Wenhui Fan( ), Tianyuan Xiao
Department of Automation, Tsinghua University, Beijing 100084, China.
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Abstract

Fuzzy Cognitive Map (FCM) is an inference network, which uses cyclic digraphs for knowledge representation and reasoning. Along with the extensive applications of FCMs, there are some limitations that emerge due to the deficiencies associated with FCM itself. In order to eliminate these deficiencies, we propose an unsupervised dynamic fuzzy cognitive map using behaviors and nonlinear relationships. In this model, we introduce dynamic weights and trend-effects to make the model more reasonable. Data credibility is also considered while establishing a machine learning model. Subsequently, we develop an optimized Estimation of Distribution Algorithm (EDA) for weight learning. Experimental results show the practicability of the dynamic FCM model. In comparison to the other existing algorithms, the proposed algorithm has better performance in terms of convergence and stability.

Keywords

Fuzzy Cognitive Map (FCM)Estimation of Distribution Algorithm (EDA)nonlinear relationmachine learning

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Tsinghua Science and Technology
Volume 20 Issue 3,
June 2015
Pages 285-292
DOI: 10.1109/TST.2015.7128941
Cite this article:
Liu B, Fan W, Xiao T. Unsupervised Dynamic Fuzzy Cognitive Map. Tsinghua Science and Technology, 2015, 20(3): 285-292. https://doi.org/10.1109/TST.2015.7128941

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Received: 04 January 2015
Revised: 15 March 2015
Accepted: 23 March 2015
Published: 19 June 2015
© The authors 2015
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