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

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

DOI QR Code

Genetic Design of Granular-oriented Radial Basis Function Neural Network Based on Information Proximity

정보 유사성 기반 입자화 중심 RBF NN의 진화론적 설계

  • 박호성 (수원대 산업기술연구소) ;
  • 오성권 (수원대 공대 전기공학과) ;
  • 김현기 (수원대 공대 전기공학과)
  • Published : 2010.02.01
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we introduce and discuss a concept of a granular-oriented radial basis function neural networks (GRBF NNs). In contrast to the typical architectures encountered in radial basis function neural networks(RBF NNs), our main objective is to develop a design strategy of GRBF NNs as follows : (a) The architecture of the network is fully reflective of the structure encountered in the training data which are granulated with the aid of clustering techniques. More specifically, the output space is granulated with use of K-Means clustering while the information granules in the multidimensional input space are formed by using a so-called context-based Fuzzy C-Means which takes into account the structure being already formed in the output space, (b) The innovative development facet of the network involves a dynamic reduction of dimensionality of the input space in which the information granules are formed in the subspace of the overall input space which is formed by selecting a suitable subset of input variables so that the this subspace retains the structure of the entire space. As this search is of combinatorial character, we use the technique of genetic optimization to determine the optimal input subspaces. A series of numeric studies exploiting some nonlinear process data and a dataset coming from the machine learning repository provide a detailed insight into the nature of the algorithm and its parameters as well as offer some comparative analysis.

Keywords

References

  1. J. A. Cumming and D. A. Wooff, "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Vol. 52, pp. 550-565, 2007. https://doi.org/10.1016/j.csda.2007.02.012
  2. S. H. Huang, "Dimensionality reduction in automatic knowledge acquisition: a simple greedy search approach," IEEE Trans. Knowledge and Data
  3. O. Buchtala, M. Klimek, and B. Sick, "Evolutionary optimization of radial basis function classifiers for data mining applications," IEEE Trans. SMC-B, Vol. 35, No. 5, pp. 928-947, 2005.
  4. A. Alexandridis, P. Patrinos, H. Sarimveis, and G. Tsekouras, "A two-stage evolutionary algorithm for variable selection in the development of RBF neural network models," Chemometrics Intell. Lab. Syst., Vol. 75, pp. 149-162, 2005. https://doi.org/10.1016/j.chemolab.2004.06.004
  5. J. Park and I. Sandberg, "Universal approximation using radial-basis function networks," Neural Comput., Vol. 3, pp. 246-257, 1991. https://doi.org/10.1162/neco.1991.3.2.246
  6. M. J. Er, W. Chen, and S. Wu, "High-speed face recognition based on discrete cosine transform and RBF neural networks," IEEE Trans. Neural Networks, Vol. 16, No. 3, pp. 679-691, 2005. https://doi.org/10.1109/TNN.2005.844909
  7. N. Xie and H. Leung, "Blind equalization using a predictive radial basis function neural network," IEEE Trans. Neural Networks, Vol. 16, No. 3, pp. 709-720, 2005. https://doi.org/10.1109/TNN.2005.845145
  8. W. Pedrycz, "Conditional fuzzy clustering in the design of radial basis function neural networks," IEEE Trans. Neural Networks, Vol. 9, No. 4, pp. 601-612, 1998. https://doi.org/10.1109/72.701174
  9. W. Pedrycz, "Conditional fuzzy c-means," Pattern Recognition Letter, Vol. 17, pp. 625-631, 1996. https://doi.org/10.1016/0167-8655(96)00027-X
  10. W. Pedrycz and K. C. Kwak, "The development of incremental models," IEEE Trans. Fuzzy Systems, Vol. 15, No. 3, pp. 507-518, 2007. https://doi.org/10.1109/TFUZZ.2006.889967
  11. J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbour, 1975.
  12. K. D. Jong, Are genetic algorithms function optimizers-, In Proc. of PPSN II (Parallel Problem Solving from Nature), Amsterdam, North Holland, 1992.
  13. W. Pedrycz, "Fuzzy clustering with a knowledge-based guidance," Pattern Recognition Letter, Vol. 25, pp. 469-480, 2004. https://doi.org/10.1016/j.patrec.2003.12.010
  14. W. Pedrycz, V. Loia, and S. Senatore, "P-FCM: a proximity-based fuzzy clustering," Fuzzy Sets and Systems, Vol. 148, pp. 21-41, 2004. https://doi.org/10.1016/j.fss.2004.03.004
  15. V. Loia, W. Pedrycz, and S. Senatore, "P-FCM: a proximity-based fuzzy clustering for user-centered web applications," Int. J. Approximate Reasoning, Vol. 34, pp. 121-144, 2003. https://doi.org/10.1016/j.ijar.2003.07.004
  16. P. Singla, K. Subbarao, and J.L. Junkins, "Direction-dependent learning approach for radial basis function networks," IEEE Trans. Neural Networks, Vol. 18, No. 1, pp. 203-222, 2007. https://doi.org/10.1109/TNN.2006.881805
  17. K. Z. Mao and G. B. Huang, "Neuron selection for RBF neural network classifier based on data structure preserving criterion," IEEE Trans. Neural Networks, Vol. 16, No. 6, pp. 1531-1540, 2005. https://doi.org/10.1109/TNN.2005.853575
  18. D. S. Yeung, W. W. Y. Ng, D.Wang, E. C. C. Tsang, and X. Z. Wang, "Localized generalization error model and its application to architecture selection for radial basis function neural network," IEEE Trans. Neural Networks, Vol. 18, No. 5, pp. 1294-1305, 2007. https://doi.org/10.1109/TNN.2007.894058
  19. X. Hong, "A fast identification algorithm for Box–Cox transformation based radial basis function neural network," IEEE Trans. Neural Networks, Vol. 17, No.4, pp. 1064-1069, 2006. https://doi.org/10.1109/TNN.2006.875986
  20. S. P. Lloyd, "Least squares quantization in PCM," IEEE Trans. Information Theory, Vol. 2, pp. 129-137, 1982.
  21. H. S. Park, W. Pedrycz, and S. K. Oh, "Evolutionary design of hybrid self-organizing fuzzy polynomial neural networks with the aid of information granulation," Expert Systems with Applications, Vol. 33, pp. 830-846, 2007. https://doi.org/10.1016/j.eswa.2006.07.006
  22. S. K. Oh and W. Pedrycz, "Identification of Fuzzy Systems by means of an Auto-Tuning Algorithm and Its Application to Nonlinear Systems," Fuzzy sets and Systems, Vol. 115, No. 2, pp. 205-230, 2000. https://doi.org/10.1016/S0165-0114(98)00174-2
  23. S. K. Oh, W. Pedrycz and B. J. Park, "Hybrid Identification of Fuzzy Rule-Based Models," Int. J. of Intelligent Systems, Vol. 17, No.1, pp. 77-103, Jan. 2002 https://doi.org/10.1002/int.1004
  24. S. K. Oh, W. Pedrycz and H. S. Park, "Hybrid Identification in Fuzzy-Neural Networks," Fuzzy Sets & Systems, Vol. 138, pp. 399-426, 2003. https://doi.org/10.1016/S0165-0114(02)00441-4
  25. H. S. Park and S. K. Oh, "Fuzzy Relation-Based Fuzzy Neural-Networks Using a Hybrid Identification Algorithm," International Journal of Control, Automations, and Systems, Vol. 1, No. 3, pp. 289-300, 2003.
  26. W. Pedrycz and K. C. Kwak, "Linguistic Models as a Framework of User-Centric System Modeling," IEEE Trans. SMC-A, Vol. 36, No. 4, pp. 727-745, 2006.
  27. W. Pedrycz, H. S. Park, and S. K. Oh, "A granular-oriented development of functional radial basis function neural networks," Vol. 72, pp. 420-435, 2008. https://doi.org/10.1016/j.neucom.2007.12.016
  28. H. S. Park and S. K. Oh, "Multi-FNN Identification Based on HCM Clustering and Evolutionary Fuzzy Granulation," International Journal of Control, Automations, and Systems, Vol. 1, No. 2, pp. 194-2002, 2003.