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A New Approach to Improve Learning in Fuzzy Cognitive Maps Using Reinforcement Learning

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Applied Computer Sciences in Engineering (WEA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1052))

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Abstract

Fuzzy Cognitive Maps (FCM) are dedicated to modeling complex dynamic systems and has been widely studied. One of those studies probed that Computing with Words (CWW) is very effective to improve the interpretability and transparency of FCM. Learning methods to calculate the weight matrix in a map are the target of hundreds of studies in various parts of the world. These methods allow the map to learn or evolve towards a better state, but always taking into account that the output must be evaluated and compared using a real scenario. Reinforcement Learning has, within its performance, one of the possible answers to this problem, aimed at improving the classification capacity of the map and thus improving the learning of its weights. This paper presents a new learning method for Fuzzy Cognitive Maps. The proposal was evaluated using international databases and the experimental results show a satisfactory performance.

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Author information

Authors and Affiliations

  1. Universidad de Camagüey, Carretera de Circunvalación Norte entre Camino Viejo de Nuevitas y Ave Ignacio Agramonte, Camagüey, Cuba

    Frank Balmaseda, Yaima Filiberto & Mabel Frias

  2. Central University of Las Villas, Carretera Camajuaní km 5.5, Santa Clara, Cuba

    Rafael Bello

Authors
  1. Frank Balmaseda
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  2. Yaima Filiberto
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  3. Mabel Frias
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  4. Rafael Bello
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Corresponding author

Correspondence to Frank Balmaseda .

Editor information

Editors and Affiliations

  1. Department of Industrial Engineering, Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

  2. Universidad Antonio Nariño, Bogotá, Colombia

    Mario Duarte-González

  3. Universidad Antonio Nariño, Bogotá, Colombia

    Sebastián Jaramillo-Isaza

  4. Universidad del Rosario, Bogotá, Colombia

    Alvaro David Orjuela-Cañon

  5. Corporación Unificada Nacional CUN, Bogotá, Colombia

    Yesid Díaz-Gutierrez

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Balmaseda, F., Filiberto, Y., Frias, M., Bello, R. (2019). A New Approach to Improve Learning in Fuzzy Cognitive Maps Using Reinforcement Learning. In: Figueroa-García, J., Duarte-González, M., Jaramillo-Isaza, S., Orjuela-Cañon, A., Díaz-Gutierrez, Y. (eds) Applied Computer Sciences in Engineering. WEA 2019. Communications in Computer and Information Science, vol 1052. Springer, Cham. https://doi.org/10.1007/978-3-030-31019-6_20

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  • DOI: https://doi.org/10.1007/978-3-030-31019-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-31018-9

  • Online ISBN: 978-3-030-31019-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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