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Tsinghua Science and Technology 2015, 20(3): 285-292 https://doi.org/10.1109/TST.2015.7128941
Open Access | Issue | Published: 19 June 2015

Unsupervised Dynamic Fuzzy Cognitive Map

Show Author's Information Boyuan Liu,  Wenhui Fan( ),  Tianyuan Xiao
Department of Automation, Tsinghua University, Beijing 100084, China.
Keywords:
machine learning, Fuzzy Cognitive Map (FCM), Estimation of Distribution Algorithm (EDA), nonlinear relation
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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Abstract Full text About this article

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.


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About this article

Unsupervised Dynamic Fuzzy Cognitive Map

Show Author's information Boyuan Liu, Wenhui Fan( ), Tianyuan Xiao
Department of Automation, Tsinghua University, Beijing 100084, China.

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: machine learning, Fuzzy Cognitive Map (FCM), Estimation of Distribution Algorithm (EDA), nonlinear relation

References(23)

[1]
Kosko, B. Fuzzy cognitive maps, International Journal of Man-Machine Studies, vol. 24, pp. 65-75, 1986.
DOI Google Scholar
[2]
Tsadiras A. K. and Margaritis, K. G. Cognitive mapping and certainty neuron fuzzy cognitive maps, Information Sciences, vol. 101, pp. 109-130, 1997.
DOI Google Scholar
[3]
Stylios C. D. and Georgoulas, G. Modeling complex logistics systems using soft computing methodology of fuzzy cognitive maps, in Automation Science and Engineering (CASE), 2011 IEEE Conference on, Trieste, Italy, 2011, pp. 72-77.
DOI
[4]
Shou, W. Fan, W. Liu, B. and Lai, Y. Knowledge map mining of financial data, Tsinghua Science and Technology, vol. 18, pp. 68-76, 2013.
DOI Google Scholar
[5]
Papageorgiou, E. I. A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques, Applied Soft Computing, vol. 11, pp. 500-513, 2011.
DOI Google Scholar
[6]
Carvalho, J. P. On the semantics and the use of fuzzy cognitive maps in social sciences, in Fuzzy Systems (FUZZ), 2010 IEEE International Conference on, Barcelona, Spain, 2010, pp. 1-6.
DOI
[7]
Hagiwara, M. Extended fuzzy cognitive maps, in Fuzzy Systems, 1992., IEEE International Conference on, San Diego, CA, USA, 1992, pp. 795-801.
[8]
Obata T. and Hagiwara, M. Neural cognitive maps (NCMs), in Systems, Man, and Cybernetics, 1997. Computational Cybernetics and Simulation., 1997 IEEE International Conference on, Orlando, FL, USA, 1997, pp. 3337-3342
[9]
Carvalho J. P. and Tome, J. A. B. Rule based fuzzy cognitive maps and fuzzy cognitive maps-a comparative study, in Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American, New York, NY, USA, 1999, pp. 115-119.
[10]
Song, H. J. Shen, Z. Q. Miao, C. Y. Liu, Z. Q. and Miao, Y. Probabilistic fuzzy cognitive map, in Fuzzy Systems, 2006 IEEE International Conference on, Vancouver, BC, Canada, 2006, pp. 1221-1228.
[11]
Salmeron, J. L. Modelling grey uncertainty with fuzzy grey cognitive maps, Expert Systems with Applications, vol. 37, pp. 7581-7588, 2010.
DOI Google Scholar
[12]
Miao, Y. Liu, Z. Q. Siew, C. K. and Miao, C. Y. Dynamical cognitive network—An extension of fuzzy cognitive map, Fuzzy Systems, IEEE Transactions on, vol. 9, pp. 760-770, 2001.
DOI Google Scholar
[13]
Iakovidis D. K. and Papageorgiou, E. Intuitionistic fuzzy cognitive maps for medical decision making, Information Technology in Biomedicine, IEEE Transactions on, vol. 15, pp. 100-107, 2011.
DOI Google Scholar
[14]
Groumpos P. P. and Stylios, C. D. Modelling supervisory control systems using fuzzy cognitive maps, Chaos, Solitons & Fractals, vol. 11, pp. 329-336, 2000.
DOI Google Scholar
[15]
Tsadiras, A. K. Comparing the inference capabilities of binary, trivalent and sigmoid fuzzy cognitive maps, Information Sciences, vol. 178, pp. 3880-3894, 2008.
DOI Google Scholar
[16]
Koulouriotis, D. E. Diakoulakis, I. E. and Emiris, D. M. Learning fuzzy cognitive maps using evolution strategies: A novel schema for modeling and simulating high-level behavior, in Evolutionary Computation, 2001. Proceedings of the 2001 Congress on, Seoul, Korea, 2001, pp. 364-371.
[17]
Kottas, T. L. Boutalis, Y. S. and Karlis, A. D. New maximum power point tracker for PV arrays using fuzzy controller in close cooperation with fuzzy cognitive networks, Energy Conversion, IEEE Transactions on, vol. 21, pp. 793-803, 2006.
DOI Google Scholar
[18]
Gedeon, T. T. D. Fung, L. C. C. Papageorgiou, E. Stylios, C. and Groumpos, P. Fuzzy cognitive map learning based on nonlinear Hebbian rule, in AI 2003: Advances in Artificial Intelligence, Gedeon T. T. D. and Fung, L. C. C. Eds. Springer Berlin Heidelberg, 2003, pp. 256-268.
DOI
[19]
Papageorgiou, E. I. Stylios, C. D. and Groumpos, P. P. Active Hebbian learning algorithm to train fuzzy cognitive maps, International Journal of Approximate Reasoning, vol. 37, pp. 219-249, 2004.
DOI Google Scholar
[20]
Parsopoulos, K. E. Papageorgiou, E. I. Groumpos, P. P. and Vrahatis, M. N. A first study of fuzzy cognitive maps learning using particle swarm optimization, in Evolutionary Computation, 2003. CEC’03. The 2003 Congress on, 2003, pp. 1440-1447.
[21]
Shou, W. Fan, W. and Liu, B. A data-based fuzzy cognitive map mining method using DE-SQP algorithm, in Asia Simulation Conference, AsiaSim 2012, October 27, 2012 – October 30, 2012, Shanghai, China, 2012, pp. 37-43.
DOI
[22]
Larrañaga P. and Lozano, J. A. Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation, vol. 2. Springer, 2002.
DOI
[23]
Scheuer E. M. and Stoller, D. S. On the generation of normal random vectors, Technometrics, vol. 4, pp. 278-281, 1962.
DOI Google Scholar
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Received: 04 January 2015
Revised: 15 March 2015
Accepted: 23 March 2015
Published: 19 June 2015
Issue date: June 2015

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