Experiments and Design of an Inference Fuzzy System

F. Benmakrouha, C. Hespel, E. Monnier, D. Quichaud

2012

Abstract

The aim of this paper is to propose a criterion to estimate the design, from experimental data, of a fuzzy inference system, when data are sparse. This lack of data is important and may improve the generalisation ability of fuzzy systems (Isao Ishibuchi, 2002). Several methods have been proposed to obtain automatic fuzzy rules from sparse training data. In (Cruz Vega Israel, 2010), the authors first construct fuzzy rules from collect data. Then, they use kernel regressions for generate training data. Another technique used when classical inference methods produce sparse fuzzy rules is a diffusion procedure based on interpolation to initialize incomplete rules (Benmakrouha, 1997), (Glorennec, 1999), (Baranyi, 1996). Our method has the advantage of occuring before initialization step and therefore avoiding unfired rules which make difficult to produce an accurate output.

References

  1. Baranyi, P. K. (1996). A general and specialised solid cutting method for fuzzy rule interpolation. J. BUSEFAL URA-CNRS, 66:13-22.
  2. Benmakrouha, F. (1997). Parameter identification in a fuzzy system with insufficient data. In Sixth IEEE International Conference on Fuzzy Systems, volume 542, page 1:537.
  3. Benmakrouha, F., Hespel, C., and Monnier, E. (2010). An algorithm for rule selection on fuzzy rule-based system applied to the treatment of diabetics and detection fraud in electronic payment. In FUZZ-IEEE.
  4. Cruz Vega Israel, W. Y. (2010). Multiple fuzzy neural networks modeling with sparse data. Neurocomputing, pages 2446-2453.
  5. Glorennec, P. Y. (1999). Algorithmes d'apprentissage pour systèmes d'inférence floue. Paris France Hermès.
  6. Isao Ishibuchi, T. Y. (2002). Performance evaluation of fuzzy partitions with different fuzzification grades. In Proceedings of the 2002 IEEE International Conference Fuzzy Systems FUZZ-IEEE'02.
  7. Lutaud, M. (1996). Identification et Controˆle de processus par réseaux Neuro-Flous. PhD thesis, Université d'Evry Val d'Essonne.
  8. Sugeno, M. and Yasukawa, T. (1993). A fuzzy-logic-based approch to qualitative modeling. IEEE Trans. on Fuzzy Systems, 1(1).
Download


Paper Citation


in Harvard Style

Benmakrouha F., Hespel C., Monnier E. and Quichaud D. (2012). Experiments and Design of an Inference Fuzzy System . In Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2012) ISBN 978-989-8565-33-4, pages 420-423. DOI: 10.5220/0004148904200423


in Bibtex Style

@conference{fcta12,
author={F. Benmakrouha and C. Hespel and E. Monnier and D. Quichaud},
title={Experiments and Design of an Inference Fuzzy System},
booktitle={Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2012)},
year={2012},
pages={420-423},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004148904200423},
isbn={978-989-8565-33-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Joint Conference on Computational Intelligence - Volume 1: FCTA, (IJCCI 2012)
TI - Experiments and Design of an Inference Fuzzy System
SN - 978-989-8565-33-4
AU - Benmakrouha F.
AU - Hespel C.
AU - Monnier E.
AU - Quichaud D.
PY - 2012
SP - 420
EP - 423
DO - 10.5220/0004148904200423