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ReNFFor: a recurrent neurofuzzy forecaster for telecommunications data

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Abstract

In this work, a dynamic neurofuzzy system for forecasting outgoing telephone calls in a University Campus is proposed. The system comprises modified Takagi–Sugeno–Kang fuzzy rules, where the rules’ consequent parts are small neural networks with unit internal recurrence. The characteristics of the proposed forecaster, which is entitled recurrent neurofuzzy forecaster, are depicted via a comparative analysis with a series of well-known forecasting models.

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References

  1. Hilas CS, Goudos SK, Sahalos JN (2006) Seasonal decomposition and forecasting of telecommunication data: a comparative case study. Technol Forecast Soc Chang 73:495–509

    Article  Google Scholar 

  2. Madden G, Joachim T (2007) Forecasting telecommunications data with linear models. Telecommun Policy 31:31–44

    Article  Google Scholar 

  3. Makridakis SG, Wheelwright SC, McGee VE (1998) Forecasting: methods and applications, 3rd edn. Wiley, New York

    Google Scholar 

  4. Holt CE (1957) Forecasting Trends and Seasonals by Exponentially Weighted Moving Averages. ONR Memorandum 52, Carnegie Institute of Technology, Pittsburg, USA. (Reprinted in 2004: Int J Forecast 20:5–10)

  5. Winters PR (1960) Forecasting sales by exponentially weighted moving averages. Manage Sci 6:324–342

    Article  MathSciNet  MATH  Google Scholar 

  6. Gardner ES Jr (1985) Exponential smoothing: the state of the art. J Forecast 4:1–28

    Article  Google Scholar 

  7. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control, 2nd edn. Holden-Day, San Francisco

    MATH  Google Scholar 

  8. Takagi T, Sugeno M (1985) Fuzzy identification of systems and its applications. IEEE Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  9. Lin C-J, Chin C–C (2004) Prediction and identification using wavelet-based recurrent fuzzy neural networks. IEEE Trans Syst Man Cybern Part B 34:2144–2154

    Article  Google Scholar 

  10. Hsu Y-C, Lin S-F (2009) Reinforcement group cooperation-based symbiotic evolution for recurrent wavelet-based neurofuzzy systems. Neurocomputing 72:2418–2432

    Article  Google Scholar 

  11. Gonzalez-Olvera MA, Tang Y (2010) Black-box identification of a class of nonlinear systems by a recurrent neurofuzzy network. IEEE Trans Neural Netw 21:672–679

    Article  Google Scholar 

  12. Tsoi AC, Back AD (1994) Locally recurrent globally feedforward networks: a critical review of architectures. IEEE Trans Neural Netw 5:229–239

    Article  Google Scholar 

  13. Hsu Y-L, Wang J-S (2009) A wiener-type recurrent neural network and its control strategy for nonlinear dynamic applications. J Process Control 19:942–953

    Article  Google Scholar 

  14. Vairappan C, Tamura H, Gao S, Tang Z (2009) Batch type local search-based adaptive neuro-fuzzy inference system (ANFIS) with self-feedbacks for time-series prediction. Neurocomputing 72:1870–1877

    Article  Google Scholar 

  15. Jassar S, Liao Z, Zhao L (2011) A recurrent neuro-fuzzy system and its application in inferential sensing. Appl Soft Comput 11:2935–2945

    Article  Google Scholar 

  16. Lin C-J, Chen C-H (2005) Identification and prediction using recurrent compensatory neuro-fuzzy systems. Fuzzy Sets Syst 150:307–330

    Article  MATH  Google Scholar 

  17. Li C, Cheng K-H (2007) Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling. Fuzzy Sets Syst 17:479–491

    MathSciNet  Google Scholar 

  18. Lee C-H, Chiu M-H (2009) Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm. Expert Syst Appl 36:8993–8999

    Article  Google Scholar 

  19. Lee C-H, Chang F-K (2008) Recurrent fuzzy neural controller design for nonlinear systems using electromagnetism-like algorithm. Far East J Exp Theor Artif Intell 1:5–22

    MATH  Google Scholar 

  20. Lee C-H, Lee Y-C (2012) Nonlinear systems design by a novel fuzzy neural system via hybridization of electromagnetism-like mechanism and particle swarm optimisation algorithms. Inf Sci 186:59–72

    Article  Google Scholar 

  21. Juang C-F (2004) A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans Syst Man Cybern Part B 34:997–1006

    Google Scholar 

  22. Juang C-F, Lin Y–Y, Tu C–C (2010) A recurrent self-evolving fuzzy neural network with local feedbacks and its application to dynamic system processing. Fuzzy Sets Syst 161:2552–2568

    Article  MathSciNet  Google Scholar 

  23. Theocharis JB (2006) A high-order recurrent neuro-fuzzy system with internal dynamics: application to the adaptive noise cancellation. Fuzzy Sets Syst 157:471–500

    Article  MathSciNet  Google Scholar 

  24. Mastorocostas PA, Theocharis JB (2002) A recurrent fuzzy neural model for dynamic system identification. IEEE Trans Syst Man Cybern Part B 32:176–190

    Google Scholar 

  25. Tao J, Wang N, Wang X (2007) Genetic algorithm based recurrent fuzzy neural network modeling of chemical processes. J Univ Comput Sci 13:1332–1343

    Google Scholar 

  26. Stavrakoudis DG, Theocharis JB (2007) Pipelined recurrent fuzzy networks for nonlinear adaptive speech prediction. IEEE Trans Syst Man Cybern Part B 37:1305–1320

    Google Scholar 

  27. Barbounis TG, Theocharis JB (2006) Locally recurrent neural networks for long-term wind speed and power prediction. Neurocomputing 69:466–496

    Article  Google Scholar 

  28. Riedmiller M, Braun H (1993) A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of IEEE International joint conference on neural networks, pp 586–591

  29. Werbos PJ (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. Thesis, Harvard University

  30. Piche SW (1994) Steepest descent algorithms for neural network controllers and filters. IEEE Trans Neural Netw 5:198–212

    Article  Google Scholar 

  31. ITU-T ICTeye Reports (2010) Available online: http://www.itu.int/ITU-D/ICTEYE/Reports.aspx [last accessed: December 2010]

  32. Fildes R, Nikolopoulos K, Crone S, Syntetos A (2008) Forecasting and operational research: a review. J Oper Res Soc 59:1150–1172

    Article  MATH  Google Scholar 

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Acknowledgments

The authors wish to acknowledge financial support provided by the Research Committee of Technological Educational Institute of Serres under grant +SAT/IC/15062011-66/11.

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Authors and Affiliations

  1. Department of Informatics and Communications, Technological Educational Institute of Serres, 62124, Serres, Greece

    Paris A. Mastorocostas & Constantinos S. Hilas

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  1. Paris A. Mastorocostas
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  2. Constantinos S. Hilas
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Correspondence to Paris A. Mastorocostas.

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Mastorocostas, P.A., Hilas, C.S. ReNFFor: a recurrent neurofuzzy forecaster for telecommunications data. Neural Comput & Applic 22, 1727–1734 (2013). https://doi.org/10.1007/s00521-012-0840-6

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  • DOI: https://doi.org/10.1007/s00521-012-0840-6

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