Fuzzy Cognitive Maps Learning Using Particle Swarm Optimization

  • Authors:

  • Elpiniki I. Papageorgiou;Konstantinos E. Parsopoulos;Chrysostomos S. Stylios;Petros P. Groumpos;Michael N. Vrahatis

  • Affiliations:

  • Department of Electrical and Computer Engineering, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, Patras, Greece GR-26500;Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, Patras, Greece GR-26110;Department of Communications, Informatics and Management, TEI of Epirus, Artificial Intelligence Research Center (UPAIRC), University of Patras, Artas, Greece GR-47100;Department of Electrical and Computer Engineering, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, Patras, Greece GR-26500;Computational Intelligence Laboratory (CI Lab), Department of Mathematics, University of Patras Artificial Intelligence Research Center (UPAIRC), University of Patras, Patras, Greece GR-26110

  • Venue:
  • Journal of Intelligent Information Systems
  • Year:
  • 2005

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Abstract

This paper introduces a new learning algorithm for Fuzzy Cognitive Maps, which is based on the application of a swarm intelligence algorithm, namely Particle Swarm Optimization. The proposed approach is applied to detect weight matrices that lead the Fuzzy Cognitive Map to desired steady states, thereby refining the initial weight approximation provided by the experts. This is performed through the minimization of a properly defined objective function. This novel method overcomes some deficiencies of other learning algorithms and, thus, improves the efficiency and robustness of Fuzzy Cognitive Maps. The operation of the new method is illustrated on an industrial process control problem, and the obtained simulation results support the claim that it is robust and efficient.