The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Design of Incremental FCM-based Recursive RBF Neural Networks Pattern Classifier for Big Data Processing

빅 데이터 처리를 위한 증분형 FCM 기반 순환 RBF Neural Networks 패턴 분류기 설계

  • Lee, Seung-Cheol (Dept. of Electronic Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Dept. of Electronic Engineering, The University of Suwon)
  • Received : 2016.03.02
  • Accepted : 2016.04.08
  • Published : 2016.06.01
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Abstract

In this paper, the design of recursive radial basis function neural networks based on incremental fuzzy c-means is introduced for processing the big data. Radial basis function neural networks consist of condition, conclusion and inference phase. Gaussian function is generally used as the activation function of the condition phase, but in this study, incremental fuzzy clustering is considered for the activation function of radial basis function neural networks, which could effectively do big data processing. In the conclusion phase, the connection weights of networks are given as the linear function. And then the connection weights are calculated by recursive least square estimation. In the inference phase, a final output is obtained by fuzzy inference method. Machine Learning datasets are employed to demonstrate the superiority of the proposed classifier, and their results are described from the viewpoint of the algorithm complexity and performance index.

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References

  1. S. K. Oh, W. Pedrycz, B. J. Park, "Polynomial-based Radial Basis Function Neural Net works realized with the Aid of Particle Swarm Optimization," Fuzzy Sets and Systems, Vol. 163, pp. 54-77, 2011. https://doi.org/10.1016/j.fss.2010.08.007
  2. W. D. Kim, D. J. Lee, and S. K. Oh, "Structural Design of FCM-based Fuzzy Inference System", CICS, pp. 63-64, 2009.
  3. W. D. Kim, S. K. Oh, H. K. Kim, "Structural Design of FCM-based Fuzzy Inference System : A Comparative Study of WLSE and LSE", The Transactions of the Korean Institute of Electrical Engineers, Vol. 59, No. 5, pp. 981-989, 2010.
  4. Yangtao Wang, Lihui Chen, Jian-Ping Mei, "Incremental Fuzzy Clustering With Multiple Medoids for Large Data", IEEE TRANSACTIONS ON FUZZY SYSTEM, Vol. 22, No. 6, pp. 1557-1568, 2014. https://doi.org/10.1109/TFUZZ.2014.2298244
  5. Richard J. Hathaway, James C. Bezdek, "Extending fuzzy and probabilistic clustering to very large data sets", Computational Statistics & Data Analysis, Vol. 51, pp. 215-234. 2006. https://doi.org/10.1016/j.csda.2006.02.008
  6. Junjie Wu, Hui Xiong, Chen Liu, Jian Chen, "A Generalization of Distance Functions for Fuzzy c-Means Clustering With Centroids of Arithmetic Means", IEEE TRANSACTIONS ON FUZZY SYSTEM, Vol. 20, No. 3, pp. 557-571. 2012. https://doi.org/10.1109/TFUZZ.2011.2179659
  7. Jonathon K. Parker, Lawrence O. Hall, "Acceleration Fuzzy-C Means Using an Estimated Subsample Size", IEEE TRANSACTIONS ON FUZZY SYSTEM, Vol. 22, No. 5, pp. 1229-1244. 2014. https://doi.org/10.1109/TFUZZ.2013.2286993
  8. Yangtao Wang, Lihui Chen, Jian-Ping Mei, "Incremental Fuzzy Clustering With Multiple Medoids for Large Data", IEEE TRANSACTIONS ON FUZZY SYSTEM, Vol. 22, No. 6, pp. 1557-1568, 2014. https://doi.org/10.1109/TFUZZ.2014.2298244
  9. Feiyan Chen, Feng Ding, "The filtering based maximum likelihood recursive least squares estimation for multiple-input single-output systems", Applied Mathematical Modelling, 2015.
  10. Cheng Wang, Tao Tang, "Recursive least squares estimation algorithm applied to a class of linear-inparameters output error moving average systems", Applied Mathematics Letters, Vol. 29, pp. 36-41. 2014.
  11. Henry Cohn, Robert Kleinberg, Balazs Szegendy and Chris Umans, "Group-theoretic Algorithms for Matrix Multiplication", IEEE Computer Society, pp. 379-388, 2005.
  12. Srisuda Aphaipanan, Yuttana Kidjaidure "Action Recognition with Adaptive RBFNN", IEEE Information and Communication Technology, pp. 1-5, 2014.