FCM Relationship Modeling for Engineering Systems

Chapter
Part of the Intelligent Systems Reference Library book series (ISRL, volume 54)

Abstract

Semantic graphs like fuzzy cognitive map (FCM) are known as powerful methodologies commonly used in control applications, as well as in relationship modeling. Besides, FCM is used as a systematic way for analyzing real-world problems with numerous known, partially known and unknown factors. This chapter discusses FCM application in relationship modeling context using some agile inference mechanisms. A sigmoid-based activation function is discussed with application in modeling hexapod locomotion gait. The activation algorithm is then added with a Hebbian weight training technique to enable automatic construction of FCMs. A numerical example case is included to show the performance of the developed model. The model is examined with perceptron learning rule as well. Finally a real-life example case is tested to evaluate the final model in terms of relationship modeling.

Supplementary material

304354_1_En_3_MOESM1_ESM.zip (1 kb)
Supplementary material 1 (zip 2 KB)

References

  1. 1.
    Kosko, B.: Fuzzy engineering. Prentice-Hall, Inc, Upper Saddle River (1996)Google Scholar
  2. 2.
    McNeill, F.M., Thro, E.: Fuzzy logic a practical approach. Academic Press Professional Inc, San Diego (1994)MATHGoogle Scholar
  3. 3.
    Motlagh, O., Tang, S.H., Ramli, A.R.: An FCM modeling for using a priori knowledge: application study in modeling quadruped walking. Neural Comput. Appl. 21(5), 1007–1015 (2010)CrossRefGoogle Scholar
  4. 4.
    Khan, M.S., Quaddus, M.: Group decision support using fuzzy cognitive maps for causal reasoning. Group Decis. Negot. 13, 463–480 (2004)CrossRefGoogle Scholar
  5. 5.
    Groumpos, P.P., Stylios, C.D.: Modeling supervisory control systems using fuzzy cognitive maps. Chaos Solitons Fract. 11, 329–336 (2000)MathSciNetCrossRefMATHGoogle Scholar
  6. 6.
    Koulouriotis, D.E., Diakoulakis, I.E., Emiris, D.M.: Learning fuzzy cognitive maps using evolution strategies: a novel schema for modeling and simulating high-level behavior. IEEE Congr. Evol. Comput (CEC2001) 1, 364–371 (2001)Google Scholar
  7. 7.
    Stach, W., Kurgan, L., Pedrycz, W., Reformat, M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 153, 371–401 (2005)MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Ghazanfari, M., Alizadeh, S., Fathian, M., Koulouriotis, D.E.: Comparing simulated annealing and genetic algorithm in learning FCM. Appl. Math. Comput. 192, 56–68 (2007)MathSciNetCrossRefMATHGoogle Scholar
  9. 9.
    Papageorgiou, E.I., De Roo, J., Huszka, C., Colaert, D.: Formalization of treatment guidelines using Fuzzy Cognitive Mapping and semantic web tools. J. Biomed. Inform. 45(1), 45–60 (2012)CrossRefGoogle Scholar
  10. 10.
    Papageorgiou, E.I.: Fuzzy cognitive map software tool for treatment management of uncomplicated urinary tract infection. Comput. Methods Programs Biomed. J. 105(3), 233–245 (2012)CrossRefGoogle Scholar
  11. 11.
    Papageorgiou, E.I., Salmeron, J.L.: Learning fuzzy grey cognitive maps using non-linear Hebbian-based approach, Int. J. Approximate Reasoning. Int. J. Approximate Reasoning 53(1), 54–65 (2012)Google Scholar
  12. 12.
    Papageorgiou, E.I.: A new methodology for Decisions in Medical Informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl. Soft Comput. 11, 500–513 (2011)CrossRefGoogle Scholar
  13. 13.
    Schneider, M., Kandel, A., Chew, G.: Automatic construction of FCMs. Fuzzy Sets Syst. 93, 161–172 (1998)CrossRefGoogle Scholar
  14. 14.
    Zhenbang, L., Zhou, L.: Advanced fuzzy cognitive maps based on OWA aggregation. Int. J. Comput. Cogn. 5(2), 31–34 (2007)Google Scholar
  15. 15.
    Motlagh, O., Tang, S.H., Ismail, N., Ramli, A.R.: An expert fuzzy cognitive map for reactive navigation of mobile robots. Fuzzy Sets Syst. 201, 105–121 (2012)Google Scholar
  16. 16.
    Biewener, A.A.: Animal locomotion: oxford animal biology series. Oxford University Press Inc., NY (2003)Google Scholar
  17. 17.
    Papageorgiou, E.I., Stylios, C.D., Groumpos, P.P.: Unsupervised learning techniques for fine-tuning fuzzy cognitive map causal links. Int. J. Hum. Comput. Stud. 64, 727–743 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • O. Motlagh
    • 1
  • S. H. Tang
    • 2
  • F. A. Jafar
    • 1
  • W. Khaksar
    • 2
  1. 1.Faculty of Manufacturing EngineeringUniversiti Teknikal Malaysia MelakaMelakaMalaysia
  2. 2.Faculty of Engineering, Department of Mechanical and ManufacturingUniversity Putra MalaysiaSelangorMalaysia

Personalised recommendations