Skip to main content
SpringerLink
Log in

Fusion of GRNN and FA for Online Noisy Data Regression

  • Published:
Neural Processing Letters Aims and scope Submit manuscript

Abstract

A new online neural-network-based regression model for noisy data is proposed in this paper. It is a hybrid system combining the Fuzzy ART (FA) and General Regression Neural Network (GRNN) models. Both the FA and GRNN models are fast incremental learning systems. The proposed hybrid model, denoted as GRNNFA-online, retains the online learning properties of both models. The kernel centers of the GRNN are obtained by compressing the training samples using the FA model. The width of each kernel is then estimated by the K-nearest-neighbors (kNN) method. A heuristic is proposed to tune the value of Kof the kNN dynamically based on the concept of gradient-descent. The performance of the GRNNFA-online model was evaluated using two benchmark datasets, i.e., OZONE and Friedman#1. The experimental results demonstrated the convergence of the prediction errors. Bootstrapping was employed to assess the performance statistically. The final prediction errors are analyzed and compared with those from other systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Specht, D. F.: A general regression neural network, IEEE Trans. Neural Network, 2(6) (1991), 568–576.

    Article  Google Scholar 

  2. Rzempoluck, E. J.: Neural network classification of EEG during camouflaged object identification, Int. J Med. Inform., 44 (1997), 169–175.

    Article  Google Scholar 

  3. Schaffner, C. and Schroder, D.: An application of general regression neural network to nonlinear adaptive control. In: Fifth European Conference on Power Electronics and Applications, Vol.4, 1993, pp. 219–224.

    Google Scholar 

  4. Hyun, B. G. and Nam, K.: Faults diagnoses of rotating machines by using neural nets: GRNN and BPN. In: Proceedings of the 1995 IEEE IECON 21st International Conference on Industrial Electronics, Control, and Instrumentation, Vol. 2, 1995, pp. 1456–1461.

    Google Scholar 

  5. Leung, M. T., Chen, A. S., Daouk, H.: Forecasting exchange rates using general regression neural networks, Comput. Operations Res., 27 (2000), 1093–1110.

    Article  Google Scholar 

  6. Parzen, E.: On estimation of a probability density function and mode, Ann. Math. Statist. 33 (1962), 1065–1076.

    Google Scholar 

  7. Grossberg, S.: Adaptive pattern recognition and universal recoding II: Feedback, expectation, olfaction and illusions, Bio. Cyber. 23 (1976), 187–202.

    Google Scholar 

  8. Carpenter, G. A., Grossberg, S. and Rosen, D. B.: Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system, Neural Network, 4 (1991), 759–771.

    Article  Google Scholar 

  9. Kosko, B.: Fuzzy entropy and conditioning, Inform. Sci. 40 (1986), 165–174.

    Article  Google Scholar 

  10. Brezmes, J., Llobet, E., Vilanova, X., Orts, J., Saiz, G., and Correig, X. J.: Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples, Sensors Actuators B, 80 (2001), 41–50.

    Article  Google Scholar 

  11. Araujo, R. and de Almeida, T.: Learning sensor-based navigation of a real mobile robot in unknown worlds, IEEE Trans. Syst. Man Cybern.—B, 29(2) (1999), 164–178.

    Article  Google Scholar 

  12. Araujo, R. and de Almeida, A. T: Fuzzy ART based approach for real-time map building, In: Proceedings of the 5th International Workshop on Advance Motion Control, AMC '98-Coimbra, 1998, pp. 623–628.

  13. Cinque, L. Foresti, G.L. Gumina, A. and Levialdi, S.: A modified FuzzyART for image segmentation, In: Proceedings of the 11th International Conference on Image Analysis and Processing, 2001, pp. 102–107.

  14. Park, S.: Neural networks and customer grouping in e-commerce: a framework using fuzzy ART In: Proceedings of Academia/Industry Working Conference on Research Challenges, 2000, pp. 331–336.

  15. Lee E. W. M., Yuen R.K.K., Lo, S.M. and Lam, K. C.: Probabilistic inference with maximum entropy for prediction of flashover in single compartment fire, Adv. Eng. Informatics, 16 (2002), 179–191.

    Article  Google Scholar 

  16. Lee, E. W. M., Lim, C. P. and Yuen, R. K. K.: A Novel Neural Network Model for Online Noisy Data Regression and Classification In: Proceedings of International Conference on Computational Intelligence for Modelling Control and Automation, Vienna, Austria, 12–14 February, 2003, pp. 95–105.

  17. Lee, E. W. M. Lim, C.P. Yuen, R. K. K. and Lo, S. M.: A Hybrid Neural Network Model for Noisy Data Regression, IEEE Trans. Syst. Man Cybern B, accepted.

  18. Lim, C.P. and Harrison, R.F.: An incremental adaptive network for on-line supervised learning and probability estimation, Neural Network, 10 (5) (1997), 925–939.

    Article  Google Scholar 

  19. Specht, D. F.: Enhancements to probabilistic neural network, IJCNN., International Joint Conference on Neural Network, Vol. 1, 1992, pp. 761–768.

    Google Scholar 

  20. Kohonen, T.: The Self-organizing map, Proc. IEEE, 78 (9) (1990), 1464–1480.

    Article  Google Scholar 

  21. Bezdek, J.B.: A convergence theorem for the fuzzy ISODATA clustering algorithms, IEEE Trans. Pattern Anal. Machine Intell. PAMI-2 (1980), 1–8.

    Google Scholar 

  22. Bishop, C.: Neural Network for Pattern Recognition, Clarendon Press, Oxford.

  23. Sarle, W. S. (ed.): Neural Network FAQ, 2001, part 2 of 7: Learning, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/ FAQ.html.

  24. Efron, B.: Bootstrap Methods: Another Look at the Jackknife, Ann. Stat., 7 (1979), 1–26.

    Google Scholar 

  25. Efron, B.: Nonparametric standard errors and confidence intervals, Can. J Stat. 9 (1981), pp. 139–172.

    Google Scholar 

  26. Carney, J. and Cunningham, P.: Tuning diversity in bagged ensembles, Int. J Neural Syst. 10 (2000), 267–280.

    Google Scholar 

  27. Friedman, J.: Multivariate adaptive regression splines (with discussion), Ann. Stat. 19(1) (1991), 1–141.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fire Safety and Disaster Prevention Research Group, Department of Building and Construction, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong (SAR), P. R. China

    Richard K. K. Yuen, Eric W. M. Lee & Grace W. Y. Cheng

  2. School of Electrical and Electronic Engineering, University of Science Malaysia, Penang, Malaysia

    C. P. Lim

Authors
  1. Richard K. K. Yuen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric W. M. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. C. P. Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Grace W. Y. Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yuen, R.K.K., Lee, E.W.M., Lim, C.P. et al. Fusion of GRNN and FA for Online Noisy Data Regression. Neural Processing Letters 19, 227–241 (2004). https://doi.org/10.1023/B:NEPL.0000035614.53039.c3

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/B:NEPL.0000035614.53039.c3

Search

Navigation