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Change a Sequence into a Fuzzy Number

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Advanced Data Mining and Applications (ADMA 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6441))

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Abstract

In general in the literature practitioners transform of real numbers into fuzzy numbers to the median or average, so they follow the probabilistic path. However, theoreticians do not investigate transformations of real numbers into fuzzy numbers when they analyse fuzzy numbers. They usually operate only on the fuzzy data. In the paper we describe an algorithm for transforming a sequence of real numbers into a fuzzy number. The algorithms presented are used to transform multidimensional matrices constructed from times series into fuzzy matrices. They were created for a special fuzzy number and using it as an example we show how to proceed. The algorithms were used in one of the stages of a model used to forecast pollution concentrations with the help of fuzzy numbers. The data used in the computations came from the Institute of Meteorology and Water Management (IMGW).

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References

  1. Rigatosa, G., Zhangb, Q.: Fuzzy Model Validation Using the Local Statistical Approach. Fuzzy Sets and Systems 160(7), 882–904 (2009)

    Article  MathSciNet  Google Scholar 

  2. Lee, C., Liu, A., Chen, W.: Pattern Discovery of Fuzzy Time Series for Financial Prediction. IEEE Trans. Knowl. Data Eng. 18(5), 613–625 (2006)

    Article  Google Scholar 

  3. Jiang, W., Wang, P.: Research on Interval Prediction of Nonlinear Chaotic Time Series Based on New Neural Networks. In: Proc. 6th World Congress Intell. Control and Automation, pp. 2835–2839 (2006)

    Google Scholar 

  4. Gautam, A.K., Chelani, A.B., Jain, V.K., Devotta, S.: A New Scheme to Predict Chaotic Time Series of Air Pollutant Concentrations using Artificial Neural Network and Nearest Neighbor Searching. Atmospheric Environ. 42(18), 4409–4417 (2008)

    Article  Google Scholar 

  5. Aznarte, M.J.L., Benítez Sánchez, J.M., Nieto Lugilde, D., de Linares Fernández, C., Díaz de la Guardia, C., Alba Sánchez, F.: Forecasting Airborne Pollen Concentration Time Series with Neural and Neuro-Fuzzy Models. Expert Systems with Applications 32(4), 1218–1225 (2007)

    Article  Google Scholar 

  6. Zadeh, L.A.: Fuzzy Sets. Inf. and Control 8, 338–353 (1965)

    Article  MATH  Google Scholar 

  7. Klir, G., Folger, T.: Fuzzy Sets, Uncertainty and Information. Prentice Hall PTR, Englewood Cliffs (1988)

    MATH  Google Scholar 

  8. Zimmerman, H.J.: Fuzzy Set Theory and Its Applications, 2nd edn. Kluwer, Dordrecht (1991)

    Book  Google Scholar 

  9. Domańska, D., Wojtylak, M.: Development Data Serving to Forecasting Pollution Concentrations. In: Decision Support System, Katowice, pp. 351–359 (2009) (in Polish)

    Google Scholar 

  10. Domańska, D., Wojtylak, M.: Selection Criteria of Forecast Pollution Concentrations Using Collateral Informations. In: Computer Methods and Systems, Kraków, pp. 213–218 (2009)

    Google Scholar 

  11. Dubois, D., Prade, H.: Ranking Fuzzy Numbers in the Setting of Possibility Theory. Information Sciences 30, 183–224 (1983)

    Article  MathSciNet  MATH  Google Scholar 

  12. Sanchez, E.: Solutions of Fuzzy Equations with Extended Operations. Fuzzy Sets and Systems 12, 237–248 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  13. Ośródka, L., Wojtylak, M., Krajny, E., Dunal, R., Klejnowski, K.: Application Data Mining for Forecasting of High-level Air Pollution in Urban-industrial Area in Southern Poland. In: Proc. of the 10th Int. Conf. on Harmonisation within Atmospheric Dispers. Modelling for Regulatory Purposes, pp. 664–668 (2005)

    Google Scholar 

  14. Domańska, D., Wojtylak, M.: Air Pollution Forecasting Model Control. Accepted for the Journal of Medical Informatics & Technologies

    Google Scholar 

  15. Hyndman, R.J., Koehler, A.B.: Another Look at Measures of Forecast Accuracy. International Journal of Forecasting 22(4), 679–688 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Institute of Computer Science, University of Silesia, Bȩdzińska 39, 41200, Sosnowiec, Poland

    Diana Domańska

  2. Institute of Meteorology and Water Management (IMGW), Bratków 10, 40045, Katowice, Poland

    Marek Wojtylak

Authors
  1. Diana Domańska
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  2. Marek Wojtylak
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Editor information

Editors and Affiliations

  1. Faculty of Engineering and Information Technology, University of Technology Sydney, 2007, Sydney, NSW, Australia

    Longbing Cao

  2. College of Computer Science, Chongqing University, 400030, Chongqing, China

    Jiang Zhong  & Yong Feng  & 

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Domańska, D., Wojtylak, M. (2010). Change a Sequence into a Fuzzy Number. In: Cao, L., Zhong, J., Feng, Y. (eds) Advanced Data Mining and Applications. ADMA 2010. Lecture Notes in Computer Science(), vol 6441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17313-4_6

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  • DOI: https://doi.org/10.1007/978-3-642-17313-4_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-17312-7

  • Online ISBN: 978-3-642-17313-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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