, Volume 33, Issue 2, pp 175–188 | Cite as

Re-approaching fuzzy cognitive maps to increase the knowledge of a system

  • Mpelogianni Vassiliki Email author
  • Groumpos Peter P. 
Original Article


Fuzzy cognitive maps is a system modeling methodology which applies mostly in complex dynamic systems by describing causal relationships that exist between its parameters called concepts. Fuzzy cognitive map theories have been used in many applications but they present several drawbacks and deficiencies. These limitations are addressed and analyzed fuzzy cognitive map theories are readdressed. A new novel approach in modelling fuzzy cognitive maps is proposed to increase the knowledge of the system and overcome some of its limitations. The state space approach is used for the new model to disaggregate the concepts into different categories. The disaggregation of the concepts into state concepts, input concepts and output concepts is mathematically formulated. The proposed method and the new model is used for the calculation of a building’s energy consumption and the management of its load. Simulations are performed as a case study testing the new proposed method. The problem of the high energy consumption of the building sector is studied using the new fuzzy cognitive map model. Discussions of the obtained results along with future research directions are provided.


Energy efficiency Building energy management Knowledge Fuzzy cognitive maps State equations 


  1. Aitken DW (2003) Transitioning to a renewable energy future. ISES White PaperGoogle Scholar
  2. Anninou AP, Groumpos PP, Panagiotis P (2013) Modeling health diseases using competitive fuzzy cognitive maps. In: IFIP International Conference on Artificial Intelligence Applications and Innovations, pp 88–95. SpringerGoogle Scholar
  3. Axelrode R (1976) The analysis of cognitive maps. Struct Decis 55–73Google Scholar
  4. Bourgani E, Stylios CD, Manis G, Georgopoulos VC (2014) Time dependent fuzzy cognitive maps for medical diagnosis. In: Hellenic conference on artificial intelligence (pp 544–554). Springer, ChamGoogle Scholar
  5. Bueno S, Salmeron JL (2009) Benchmarking main activation functions in fuzzy cognitive maps. Expert Syst Appl 36(3):5221–5229CrossRefGoogle Scholar
  6. Chen YC, Teng CC (1995) A model reference control structure using a fuzzy neural network. Fuzzy sets Syst 73(3):291–312MathSciNetCrossRefzbMATHGoogle Scholar
  7. Eastman C (1976) General purpose building description systems. Comput Aided Des 8(1):17–26CrossRefGoogle Scholar
  8. Eastman CM (1999) Building product models: computer environments, supporting design and construction. CRC press, Boca RatonGoogle Scholar
  9. Filippín C, Larsen SF (2007) Energy efficiency in buildings. In book: Energy Efficiency, Recovery and Storage ISBN, Chapter: 11, Publisher: Konrad A. Hofman, Nova Science Publishers, pp 223–245Google Scholar
  10. Groumpos PP (2010) Fuzzy cognitive maps: basic theories and their application to complex systems. Fuzzy cognitive maps, pp 1–22. Springer, BerlinCrossRefGoogle Scholar
  11. Groumpos PP, Anninou AP (2012) A theoretical mathematical modeling of parkinson’s disease using fuzzy cognitive maps. In: Bioinformatics & Bioengineering (BIBE), 2012 IEEE 12th International Conference on, pp 677–682. IEEEGoogle Scholar
  12. Groumpos P, Gkountroumani V (2014) A new control strategy for modeling wind energy systems using fuzzy cognitive maps. J Energy Power Eng 8(11)Google Scholar
  13. Groumpos PP, Mpelogianni V (2016) An overview of fuzzy cognitive maps for energy efficiency in intelligent buildings. In: Information, Intelligence, Systems & Applications (IISA), 2016 7th International Conference on (pp. 1–6). IEEEGoogle Scholar
  14. Groumpos PP, Stylios CD (2000) Modelling supervisory control systems using fuzzy cognitive maps. Chaos Solitons Fractals 11(1):329–336MathSciNetCrossRefzbMATHGoogle Scholar
  15. Karagiannis IE, Groumpos PP (2013) Input-sensitive fuzzy cognitive maps. IJCSI Int J Comput Sci 2013:143–151Google Scholar
  16. Kosko B (1986) Fuzzy cognitive maps. Int J Man Mach Stud 24(1):65–75CrossRefzbMATHGoogle Scholar
  17. Koulouriotis DE, Diakoulakis IE, Emiris DM (2001) Anamorphosis of fuzzy cognitive maps for operation in ambiguous and multi-stimulus real world environments. In: Fuzzy Systems, 2001. The 10th IEEE International Conference on (Vol. 3, pp. 1156–1159). IEEEGoogle Scholar
  18. Mpelogianni V, Groumpos PP (2015) Using fuzzy control methods for increasing the energy efficiency of buildings. Int J Monit Surveill Technol Res (IJMSTR) 3(4):1–22CrossRefGoogle Scholar
  19. Mpelogianni V, Groumpos PP (2016a) Towards a new approach of fuzzy cognitive maps. In: Information, Intelligence, Systems and Applications (IISA), 2016 7th International Conference on. IEEEGoogle Scholar
  20. Mpelogianni V, Groumpos PP (2016b) A revised approach in modeling fuzzy cognitive maps. In: Control and Automation (MED), 2016 24th Mediterranean Conference on (pp 350–354). IEEEGoogle Scholar
  21. Mpelogianni V, Marnetta P, Groumpos PP (2015) Fuzzy cognitive maps in the service of energy efficiency. IFAC-PapersOnLine 48(24):1–6CrossRefGoogle Scholar
  22. Nguyen T, Aiello M (2013) Energy intelligent buildings based on user activity: a survey. Energ Build 56:244–257CrossRefGoogle Scholar
  23. Ntarlas O, Groumpos P (2015) Unsupervised learning methods for foreign investment using fuzzy cognitive maps. In: Information, Intelligence, Systems and Applications (IISA), 2015 6th International Conference on, pp 1–5. IEEEGoogle Scholar
  24. Ogata K (1967) State space analysis of control systemsGoogle Scholar
  25. Papageorgiou E, Stylios C (2008) Fuzzy cognitive maps. Handb Granul Comput 123:755–774Google Scholar
  26. Papageorgiou E, Stylios CD, Groumpos PP (2004) Active hebbian learning algorithm to train fuzzy cognitive maps. Int J Approx Reason 37(3):219–249MathSciNetCrossRefzbMATHGoogle Scholar
  27. Runkler TA (1996) Extended defuzzification methods and their properties. In Fuzzy Systems, 1996. In: Proceedings of the Fifth IEEE International Conference on, volume 1, pp 694–700. IEEEGoogle Scholar
  28. Schlueter A, Thesseling F (2009) Building information model based energy/exergy performance assessment in early design stages. Autom Constr 18(2):153–163CrossRefGoogle Scholar
  29. Schneider M, Shnaider E, Kandel A, Chew G (1995) Constructing fuzzy cognitive maps. In Fuzzy Systems, 1995. In: International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium., Proceedings of 1995 IEEE Int (vol 4, pp 2281–2288). IEEEGoogle Scholar
  30. Song HJ, Miao CY, Shen ZQ, Roel W, Maja DH, Francky C (2010a) Design of fuzzy cognitive maps using neural networks for predicting chaotic time series. Neural Netw 23(10):1264–1275CrossRefGoogle Scholar
  31. Song H, Miao C, Roel W, Shen Z, Catthoor F (2010b) Implementation of fuzzy cognitive maps based on fuzzy neural network and application in prediction of time series. IEEE Trans Fuzzy Syst 18(2):233–250Google Scholar
  32. So A, Wong A, Wong K (1999) A new definition of intelligent buildings for Asia. Facilities 17(12/13):485–491CrossRefGoogle Scholar
  33. Vaščák J, Madarász L (2010) Adaptation of fuzzy cognitive maps-a comparison study. Acta Polytech Hung 7(3):109–122Google Scholar
  34. Vergini ES, Groumpos PP (2016) A new conception on the fuzzy cognitive maps method. IFACPapersOnLineGoogle Scholar
  35. Vergini E, Costoula T, Groumpos P (2015) Modeling zero energy building with a three–level fuzzy cognitive map. Recent Adv Environ Earth Sci Econ, 275–280Google Scholar
  36. Wang S (2010) Intelligent buildings and building automation. Spon Press, LondonGoogle Scholar
  37. Wong JK, Li H, Wang S (2005) Intelligent building research: a review. Autom constr 14(1):143–159CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringUniversity of PatrasRionGreece

Personalised recommendations