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

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Design of Incremental K-means Clustering-based Radial Basis Function Neural Networks Model

증분형 K-means 클러스터링 기반 방사형 기저함수 신경회로망 모델 설계

  • Park, Sang-Beom (Dept. of Electrical Engineering, The University of Suwon) ;
  • Lee, Seung-Cheol (Dept. of Electrical Engineering, The University of Suwon) ;
  • Oh, Sung-Kwun (Dept. of Electrical Engineering, The University of Suwon)
  • Received : 2017.01.09
  • Accepted : 2017.03.29
  • Published : 2017.05.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, the design methodology of radial basis function neural networks based on incremental K-means clustering is introduced for learning and processing the big data. If there is a lot of dataset to be trained, general clustering may not learn dataset due to the lack of memory capacity. However, the on-line processing of big data could be effectively realized through the parameters operation of recursive least square estimation as well as the sequential operation of incremental clustering algorithm. Radial basis function neural networks consist of condition part, conclusion part and aggregation part. In the condition part, incremental K-means clustering algorithms is used tweights of the conclusion part are given as linear function and parameters are calculated using recursive least squareo get the center points of data and find the fitness using gaussian function as the activation function. Connection s estimation. In the aggregation part, a final output is obtained by center of gravity method. Using machine learning data, performance index are shown and compared with other models. Also, the performance of the incremental K-means clustering based-RBFNNs is carried out by using PSO. This study demonstrates that the proposed model shows the superiority of algorithmic design from the viewpoint of on-line processing for big data.

Keywords

References

  1. N. Pombo, N. Garcia, V. Felizardo, K. Bousson, "Big data reduction using RBFNN: A predictive model for ECG waveform for eHealth platform integration", e-Health Networking, Applications and Services, pp. 66-70, 2014.
  2. S. C. Lee, S. K. Oh, "Design of Incremental FCM-based Recursive RBF Neural Networks Pattern Classifier for Big Data Processing", The Transactions of the Korean Institute of Electrical Engineers, Vol. 65, No. 6, pp. 1070-1079, 2016. https://doi.org/10.5370/KIEE.2016.65.6.1070
  3. Y. Ji, F. Ran, C. Ma, David J. Lilja, "A hardware implementation of a radial basis function neural network using stochastic logic", Design, Automation & Test in Europe Conference & Exhibition, pp. 880-883, 2015.
  4. J. P. Mei, Y. Wang, L. Chen, C. Miao, "Incremental fuzzy clustering for document categorization", IEEE International Conference on Fuzzy Systems, pp. 1518-1525, 2014.
  5. Akash Dutt Dubey, "K-Means based radial basis function neural networks for rainfall prediction", International Conference on Trends in Automation, Communications and Computing Technology, Vol. 01, No. 19, pp. 103-109, 2015
  6. G. Chowdhary, Hassan A. Kingravi, Jonathan P. How, Patricio A. Vela, "Bayesian Nonparametric Adaptive Control Using Gaussian Process", IEEE Transactions on Neural Networks and Learning Systems, Vol. 26, No. 3, pp. 537-550, 2015. https://doi.org/10.1109/TNNLS.2014.2319052
  7. C. Wang, T. Tang, "Recursive least squares estimation algorithm applied to a class of linear-in-parameters output error moving average systems", Applied Mathematics Letters, Vol. 29, pp. 36-41. 2014.
  8. Y. H. Pyo, K. H. Lee, K. H. You, "Earthquake magnitude estimation using Recursive Least Squares", IEEE International Conference on Cloud Computing and Big Data, pp. 394-397, 2016
  9. J. S. Bae, S. K. Oh, H. K. Kim, "Design of Fingerprints Identification Based on RBFNN Using Image Processing Techniques", The Transactions of Korean Institute of Electrical Engineers, Vol. 65, No. 6, pp. 1060-1069, 2016. https://doi.org/10.5370/KIEE.2016.65.6.1060
  10. 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 Korean Institute of Electrical Engineers, Vol. 59, No. 5, pp. 981-989, 2010
  11. S. K. Oh, Y. H. Kim, H. S. Park, J. T. Kim, "Design of Data-centroid Radial Basis Function Neural Network with Extended Polynomial Type and Its Optimization", The Transactions of Korean Institute of Electrical Engineers, Vol. 60, No. 3, pp. 639-647, 2011. https://doi.org/10.5370/KIEE.2011.60.3.639
  12. J. S. R. Jang, "ANFIS : Adaptive-network-based fuzzy inference system", IEEE Transaction on Systems, Man, and Cybernetics, Vol. 23, No. 3, pp. 665-685, 1993. https://doi.org/10.1109/21.256541
  13. L. P. Maguire, B. Roche, T. M. McGinnity, L. J. McDaid, "Predicting a chaotic time series using a fuzzy neural network", Information Science, Vol. 112, pp. 125-136, 1998. https://doi.org/10.1016/S0020-0255(98)10026-9
  14. S. K. Oh, W. D. Kim, H. S. Park, M. H. Son, "Identification Methodology of FCM-based Fuzzy Model Using Particle Swarm Optimization", The Transactions of Korean Institute of Electrical Engineers, Vol. 60, No. 1, pp. 184-192, 2011. https://doi.org/10.5370/KIEE.2011.60.1.184
  15. H. S. Park, Y, H. Jin, S. K Oh, "Evolutionary Design of Radial Basis Function-based Polynomial Neural Network with the aid of Information Granulation", The Transactions of Korean Institute of Electrical Engineers, Vol. 60, No. 4, pp. 862-870, 2011. https://doi.org/10.5370/KIEE.2011.60.4.862
  16. W. Pedrycz, K. C. Kwak, "Linguistic Models as A Framework of User-Centric System Modeling", IEEE Transaction on Systems, Man, and Cybernetics, Vol. 36, No. 4, pp. 727-745, 2006 https://doi.org/10.1109/TSMCA.2005.855755