Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links

  • Authors:

  • Elpiniki I. Papageorgiou;Chrysostomos Stylios;Peter P. Groumpos

  • Affiliations:

  • Laboratory for Automation and Robotics, Department of Electrical & Computer Engineering, Artificial Intelligence Research Center (UPAIRC), University of Patras, Rion 26500, Greece;Department Communications, Informatics and Management, Technological Educational Institute (TEI) of Epirus, Artas;Laboratory for Automation and Robotics, Department of Electrical & Computer Engineering, Artificial Intelligence Research Center (UPAIRC), University of Patras, Rion 26500, Greece

  • Venue:
  • International Journal of Human-Computer Studies
  • Year:
  • 2006

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Abstract

Fuzzy Cognitive Maps (FCMs) constitute an attractive knowledge-based methodology, combining the robust properties of fuzzy logic and neural networks. FCMs represent causal knowledge as a signed directed graph with feedback and provide an intuitive framework which incorporates the experts' knowledge. FCMs handle available information and knowledge from an abstract point of view. They develop behavioural model of the system exploiting the experience and knowledge of experts. The construction of FCMs is based mainly on experts who determine the structure of FCM, i.e. concepts and weighted interconnections among concepts. But this methodology may not be a sufficient model of the system because the human factor is not always reliable. Thus the FCM model of the system may requires restructuring which is achieved through adjustment the weights of FCM interconnections using specific learning algorithms for FCMs. In this article, two unsupervised learning algorithms are presented and compared for training FCMs; how they define, select or fine-tuning weights of the causal interconnections among concepts. The implementation and results of these unsupervised learning techniques for an industrial process control problem are discussed. The simulations results of training the process system verify the effectiveness, validity and advantageous characteristics of those learning techniques for FCMs.