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Kernel-Based Fuzzy C-Means Clustering Algorithm for RBF Network Initialization

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Intelligent Decision Technologies 2016 (IDT 2016)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 56))

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Abstract

Designing an effective structure of the RBF network is the task carried-out at the network initialization phase. Usual approach to deal with the problem is to decide on the number of hidden units and to apply a clustering algorithm to calculate cluster centroids. Clustering techniques have a strong influence on the performance of the RBF networks. The paper focuses on the radial basis function neural network initialization problem and the implementation of the kernel-based fuzzy C-means clustering algorithm, as an alternative method for the RBF networks initialization. Performance of the RBFNs initialized using the kernel-based fuzzy clustering algorithm is compared with several other clustering techniques, including k-means, fuzzy C-means and X-means.

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References

  1. Asuncion, A., Newman, D.J.: UCI Machine Learning Repository. http://www.ics.uci.edu/~mlearn/MLRepository.html. University of California, School of Information and Computer Science, Irvine (2007)

  2. Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer Publishers, New York (1981)

    Book  MATH  Google Scholar 

  3. Broomhead, D.S., Lowe, D.: Multivariable Functional Interpolation and Adaptive Networks. Complex Syst. 2, 321–355 (1988)

    MathSciNet  MATH  Google Scholar 

  4. Chen, H., Gong, Y., Hong, X., Chen. S.: A Fast Adaptive Tunable RBF Network for Non-stationary Systems. IEEE Trans. Syst. Man Cybern. Part B (99), 1–10 (2015). doi:10.1109/TCYB.2015.2484378

    Google Scholar 

  5. Chen, S., Billings, S.A., Grant, P.M.: Recursive hybrid algorithm for non-linear system identification using radial basis function networks. Int. J. Control 55(5), 1051–1070 (1992). doi:10.1080/00207179208934272

    Article  MathSciNet  MATH  Google Scholar 

  6. Czarnowski, I., Jędrzejowicz, P.: Agent-based approach to the design of RBF networks. Cybern. Syst. 44(2–3), 155–172 (2013)

    Article  Google Scholar 

  7. Czarnowski, I., Jędrzejowicz, J., Jędrzejowicz, P.: Designing RBFNs with similarity-based and kernel-based fuzzy C-means clustering algorithm. ACM Trans. Knowl. Discov. Data 2016 (manuscript submitted for publication)

    Google Scholar 

  8. De Corvalho, A., Brizzotti, M.M.: Combining RBF networks trained by different clustering techniques. Neural Process. Lett. 14, 227–240 (2001)

    Google Scholar 

  9. Du, K.-L.: Clustering: A neural network approach. J. Neural Netw. 23(1), 89–107 (2010). doi:10.1016/j.neunet.2009.08.007

    Article  Google Scholar 

  10. Gao, H., Feng, B., Zhu, L.: Training RBF neural network with hybrid particle swarm optimisation. In: Weng, J., et al. (eds.) ISNN 2006, LNCS 3971, pp. 577–583. Springer, Berlin Heidelberg (2006)

    Google Scholar 

  11. Garg, S., Patra, K., Khetrapal, V., Pal, S.K., Chakraborty, D.: Genetically evolved radial basis function network based prediction of drill flank wear. Eng. Appl. Artif. Intell. 23, 1112–1120 (2010)

    Article  Google Scholar 

  12. Graves, D., Pedrycz, W.: Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst. 161, 522–543 (2010). doi:10.1016/j.fss.2009.10.021

    Article  MathSciNet  Google Scholar 

  13. Grover, N.: A study of various fuzzy clustering algorithms. Int. J. Eng. Res. 3(3), 177–181 (2014)

    Article  MathSciNet  Google Scholar 

  14. Havens, T.C., Bezdek, J.C., Palaniswami, M.: Cluster validity for kernel fuzzy clustering. In: Proceedings of 2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE. Brisbane, QLD, pp. 1–8 (2012). doi:10.1109/FUZZ-IEEE.2012.6250820

  15. Huang, G.-B., Saratchandra, P., Sundararajan, N.: A generalized growing and pruning RBF(GGAP-RBF) neural network for function approximation. IEEE Trans. Neural Netw. 16(1), 57–67 (2005). doi:10.1109/TNN.2004.836241

    Article  Google Scholar 

  16. Krishnaiah, P.R., Kanal, L.N.: Handbook of Statistics 2: Classification, Pattern Recognition and Reduction of Dimensionality. North Holland, Amsterdam (1982)

    Google Scholar 

  17. Li, Z., Tang, S., Xue, J., Jiang, J.: Modified FCM clustering based on kernel mapping. In: Proceedings of the International Conference on Society for Optical Engineering, vol. 4554, pp. 241–245 (2001). doi:10.1117/12.441658

  18. Mashor, M.Y.: Hybrid training algorithm for RBF network. Int. J. Comput. Internet Manag. 8(2), 50–65 (2000)

    Google Scholar 

  19. Mekhmoukh, A., Mokrani, K., Cheriet, M.: A modified Kernelized Fuzzy C-Means algorithm for noisy images segmentation: application to MRI images. IJCSI Int. J. Comput. Sci. Issues 9(1), 1172–1176 (2012)

    Google Scholar 

  20. Moody, J., Darken, C.J.: Fast learning in networks of locally-tuned processing units. Neural Comput. 1(2), 281–294 (1989)

    Article  Google Scholar 

  21. Niros, A.D., Tsekouras, G.E.: A novel training algorithm for RBF neural network using a hybrid fuzzy clustering approach. J. Fuzzy Sets Syst. 193, 62–84 (2012). doi:10.1016/j.fss.2011.08.011

    Article  MathSciNet  Google Scholar 

  22. Pelleg, D., Moore, A.: X-means: Extending K-means with efficient estimation of the number of clusters. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 727–734 (2000)

    Google Scholar 

  23. Platt, J.C.: A resource-allocating network for function interpolation. Neural Comput. 3(2), 213–225 (1991)

    Article  MathSciNet  Google Scholar 

  24. Ruspini, E.H.: Numerical methods for fuzzy clustering. J. Inf. Sci. 2(3), 319–350 (1970)

    Article  MATH  Google Scholar 

  25. Sánchez, A.V.D.: Searching for a solution to the automatic RBF network design problem. Neurocomputing 42(1–4), 147–170 (2002)

    Article  MATH  Google Scholar 

  26. Wong, Y.W., Seng, K.P., Ang, L.-M.: Radial basis function neural network with incremental learning for face recognition. IEEE Trans. Syst. Man Cybern. Part B—Cybern. 41(4), 1–16 (2011). doi:10.1109/TSMCB.2010.2101591

    Google Scholar 

  27. Zhou, S., Gan, J.Q.: Mercel kernel fuzzy c-means algorithm and prototypes of clusters, In: Proceedings of the International Conference on Data Engineering and Automated Learning. Lecture Notes in Computer Science, vol. 3177, pp. 613–618 (2004). doi:10.1007/978-3-540-28651-6_90

    Google Scholar 

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Authors and Affiliations

  1. Department of Information Systems, Gdynia Maritime University, Morska 83, 81-225, Gdynia, Poland

    Ireneusz Czarnowski & Piotr Jędrzejowicz

Authors
  1. Ireneusz Czarnowski
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  2. Piotr Jędrzejowicz
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Corresponding author

Correspondence to Ireneusz Czarnowski .

Editor information

Editors and Affiliations

  1. Maritime University, Gdynia, Poland

    Ireneusz Czarnowski

  2. Artificial Intelligence Department, Universidad Politécnica de Madrid, Boadilla del Monte, Madrid, Spain

    Alfonso Mateos Caballero

  3. KES International, Shoreham-by-sea, United Kingdom

    Robert J. Howlett

  4. University of Canberra, Canberra, South Australia, Australia

    Lakhmi C. Jain

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Czarnowski, I., Jędrzejowicz, P. (2016). Kernel-Based Fuzzy C-Means Clustering Algorithm for RBF Network Initialization. In: Czarnowski, I., Caballero, A., Howlett, R., Jain, L. (eds) Intelligent Decision Technologies 2016. IDT 2016. Smart Innovation, Systems and Technologies, vol 56. Springer, Cham. https://doi.org/10.1007/978-3-319-39630-9_28

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  • DOI: https://doi.org/10.1007/978-3-319-39630-9_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39629-3

  • Online ISBN: 978-3-319-39630-9

  • eBook Packages: EngineeringEngineering (R0)

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