APPLICATION OF FUZZY COGNITIVE MAP TO PREDICT OF EFFECTIVENESS OF BIKE SHARING SYSTEMS

Aleksander Jastriebow

a.jastriebow@tu.kielce.pl
Politechnika Świętokrzyska, Katedra Systemów Informatycznych (Poland)

Łukasz Kubuś


Politechnika Świętokrzyska, Katedra Systemów Informatycznych (Poland)

Katarzyna Poczęta


Politechnika Świętokrzyska, Katedra Systemów Informatycznych (Poland)

Abstract

This paper proposes application of fuzzy cognitive map with evolutionary learning algorithms to model a system for prediction of effectiveness of bike sharing systems. Fuzzy cognitive map was constructed based on historical data and next used to forecast the number of cyclists and customers of bike sharing systems on three consecutive days. The learning process was realized with the use of Individually Directional Evolutionary Algorithm IDEA and Real-Coded Genetic Algorithm RCGA. Simulation analysis of the system for prediction of effectiveness of bike sharing systems was carried out with the use of software developed in JAVA.


Keywords:

fuzzy cognitive map, predictive model, evolutionary computation, machine learning

Acampora G., Pedrycz W., Vitiello A.: A Competent Memetic Algorithm for Learning Fuzzy Cognitive Maps. IEEE Transactions on Fuzzy Systems 23(6)/2015, 2397–2411.
  Google Scholar

Ahmadi S., Alizadeh S., Forouzideh N., Yeh C., Martin R. L., Papageorgiou E.: ICLA: Imperialist Competitive Learning Algorithm for Fuzzy Cognitive Map. Proceedings of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 2014.
  Google Scholar

Arabas J.: Wykłady z algorytmów ewolucyjnych, WNT, Warszawa 2001.
  Google Scholar

Berry A., Vamplew P.: PoD Can Mutate: A Simple Dynamic Directed Mutation Approach for Genetic Algorithms. Proceedings of AISAT 2004: The 2nd International Conference on Artificial Intelligence in Science and Technology, 2004, 200–205.
  Google Scholar

Fanaee-T H., Gama J.: Event labeling combining ensemble detectors and background knowledge. Progress in Artificial Intelligence, Springer Berlin Heidelberg, 2013, 1–15.
  Google Scholar

Froelich W., Papageorgiou E.: Extended Evolutionary Learning of Fuzzy Cognitive Maps for the Prediction of Multivariate Time-Series. Papageorgiou E.I.: Fuzzy Cognitive maps for Applied Sciences and Engineering From fundamentals to extensions and learning algorithms. Intelligent Systems Reference Library 54/2014, 121–131.
  Google Scholar

Goldberg D. E.: Algorytmy genetyczne i ich zastosowania. WNT, Warszawa 1995.
  Google Scholar

Homenda W., Jastrzebska A., Pedrycz W.: Modeling Time Series with Fuzzy Cognitive Maps. 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Beijing, China, 2014, 2055–2062.
  Google Scholar

Jastriebow A., Kubuś Ł., Poczęta K.: Learning fuzzy cognitive maps using Individually Directional Evolutionary Algorithm. In: Jastriebow A., Worwa K.: Applications of information technologies - theory and practice. Institute for Sustainable Technologies – National Research Institute, Radom 2015, 37–48.
  Google Scholar

Korejo I., Yang S., Li C.: A Directed Mutation Operator for Real Coded Genetic Algorithms. Applications of Evolutionary Computation 6024/2010, 491–500.
  Google Scholar

Kosko B.: Fuzzy cognitive maps. International Journal of Man-Machine Studies 24(1)/1986, 65–75.
  Google Scholar

Kubuś Ł.: Individually Directional Evolutionary Algorithm for Solving Global Optimization Problems - Comparative Study, International Journal of Intelligent Systems and Applications (IJISA) 7(9)/2015, 12–19.
  Google Scholar

Michalewicz Z.: Algorytmy genetyczne + struktury danych = programy ewolucyjne. WNT, Warszawa 1999.
  Google Scholar

Papageorgiou E. I.: Learning Algorithms for Fuzzy Cognitive Maps - A Review Study. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and Reviews 42(2)/2012, 150–163.
  Google Scholar

Poczęta K., Yastrebov A.: Analysis of Fuzzy Cognitive Maps with Multi-Step Learning Algorithms in Valuation of Owner-Occupied Homes. 2014 IEEE International Conference on Fuzzy Systems (FUZZIEEE), Beijing, China 2014, 1029–1035.
  Google Scholar

Poczęta K., A. YastrebovA., Papageorgiou E. I.: Learning Fuzzy Cognitive Maps using Structure Optimization Genetic Algorithm. 2015 Federated Conference on Computer Science and Information Systems (FedCSIS), Lodz, Poland, 2015, 547–554.
  Google Scholar

Song H., Miao C., Roel W., Shen Z.: Implementation of fuzzy cognitive maps based on fuzzy neural network and application in prediction of time series. IEEE Transactions on Fuzzy Systems 18(2)/2010, 233–250.
  Google Scholar

Stach W., Kurgan L., Pedrycz W., Reformat M.: Genetic learning of fuzzy cognitive maps. Fuzzy Sets and Systems 153(3)/2005, 371–401.
  Google Scholar

Stach W., Pedrycz W., Kurgan L. A.: Learning of fuzzy cognitive maps using density estimate. IEEE Trans. on Systems, Man, and Cybernetics, Part B, vol. 42(3)/2012, 900–912.
  Google Scholar

Tang P., Tseng M.: Adaptive directed mutation for real-coded genetic algorithms. Applied Soft Computing 13(1)/2013, 600–614.
  Google Scholar

Yesil E., Urbas L.: Big bang: big crunch learning method for fuzzy cognitive maps. World Acad. Sci. Eng. Technol. 71/2010, 815–8124.
  Google Scholar


Published
2017-12-21

Cited by

Jastriebow, A., Kubuś, Łukasz, & Poczęta, K. (2017). APPLICATION OF FUZZY COGNITIVE MAP TO PREDICT OF EFFECTIVENESS OF BIKE SHARING SYSTEMS. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 7(4), 70–73. https://doi.org/10.5604/01.3001.0010.7363

Authors

Aleksander Jastriebow 
a.jastriebow@tu.kielce.pl
Politechnika Świętokrzyska, Katedra Systemów Informatycznych Poland

Authors

Łukasz Kubuś 

Politechnika Świętokrzyska, Katedra Systemów Informatycznych Poland

Authors

Katarzyna Poczęta 

Politechnika Świętokrzyska, Katedra Systemów Informatycznych Poland

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