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Fuzzy Cognitive Maps Optimization for Decision Making and Prediction

Author

Listed:
  • Katarzyna Poczeta

    (Department of Information Systems, Kielce University of Technology, 25-314 Kielce, Poland)

  • Elpiniki I. Papageorgiou

    (Department of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis Campus, GR 41500 Larissa, Greece)

  • Vassilis C. Gerogiannis

    (Department of Digital Systems, Faculty of Technology, University of Thessaly, Geopolis Campus, GR 41500 Larissa, Greece)

Abstract

Representing and analyzing the complexity of models constructed by data is a difficult and challenging task, hence the need for new, more effective techniques emerges, despite the numerous methodologies recently proposed in this field. In the present paper, the main idea is to systematically create a nested structure, based on a fuzzy cognitive map (FCM), in which each element/concept at a higher map level is decomposed into another FCM that provides a more detailed and precise representation of complex time series data. This nested structure is then optimized by applying evolutionary learning algorithms. Through the application of a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This process allows discovering and describing hidden relationships among important map concepts. The paper proposes the application of the suggested nested approach for time series forecasting as well as for decision-making tasks regarding appliances’ energy consumption prediction.

Suggested Citation

  • Katarzyna Poczeta & Elpiniki I. Papageorgiou & Vassilis C. Gerogiannis, 2020. "Fuzzy Cognitive Maps Optimization for Decision Making and Prediction," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:11:p:2059-:d:447037
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    References listed on IDEAS

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    1. Jin-Young Kim & Sung-Bae Cho, 2019. "Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder," Energies, MDPI, vol. 12(4), pages 1-14, February.
    2. Spyros Makridakis & Robert L. Winkler, 1983. "Averages of Forecasts: Some Empirical Results," Management Science, INFORMS, vol. 29(9), pages 987-996, September.
    3. Karin Kandananond, 2011. "Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach," Energies, MDPI, vol. 4(8), pages 1-12, August.
    4. Konstantinos Papageorgiou & Gustavo Carvalho & Elpiniki I. Papageorgiou & Dionysis Bochtis & George Stamoulis, 2020. "Decision-Making Process for Photovoltaic Solar Energy Sector Development using Fuzzy Cognitive Map Technique," Energies, MDPI, vol. 13(6), pages 1-23, March.
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    Cited by:

    1. Konstantinos Kokkinos & Eftihia Nathanail, 2023. "A Fuzzy Cognitive Map and PESTEL-Based Approach to Mitigate CO 2 Urban Mobility: The Case of Larissa, Greece," Sustainability, MDPI, vol. 15(16), pages 1-30, August.
    2. Orang, Omid & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    3. Luz E. Gutiérrez & José Javier Samper & Daladier Jabba & Wilson Nieto & Carlos A. Guerrero & Mark M. Betts & Héctor A. López-Ospina, 2023. "Combined Framework of Multicriteria Methods to Identify Quality Attributes in Augmented Reality Applications," Mathematics, MDPI, vol. 11(13), pages 1-39, June.
    4. Katarzyna Poczeta & Elpiniki I. Papageorgiou, 2022. "Energy Use Forecasting with the Use of a Nested Structure Based on Fuzzy Cognitive Maps and Artificial Neural Networks," Energies, MDPI, vol. 15(20), pages 1-18, October.
    5. Shruthi Dakey & Sameer Deshkar & Shreya Joshi & Vibhas Sukhwani, 2023. "Enhancing Resilience in Coastal Regions from a Socio-Ecological Perspective: A Case Study of Andhra Pradesh, India," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    6. Mohammad Javad Bidel & Hossein Safari & Hannan Amoozad Mahdiraji & Edmundas Kazimieras Zavadskas & Jurgita Antucheviciene, 2022. "A Framework for Project Delivery Systems via Hybrid Fuzzy Risk Analysis: Application and Extension in ICT," Mathematics, MDPI, vol. 10(17), pages 1-22, September.
    7. Viktorija Terjanika & Jelena Pubule, 2022. "Barriers and Driving Factors for Sustainable Development of CO 2 Valorisation," Sustainability, MDPI, vol. 14(9), pages 1-16, April.

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