Browse > Article
http://dx.doi.org/10.5370/KIEE.2010.59.2.436

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

Park, Ho-Sung (수원대 산업기술연구소)
Oh, Sung-Kwun (수원대 공대 전기공학과)
Kim, Hyun-Ki (수원대 공대 전기공학과)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.59, no.2, 2010 , pp. 436-444 More about this Journal
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
Radial basis function neural network; Proximity; Context-based fuzzy C-means clustering; Information granules; Genetic algorithm;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By SCOPUS : 0
연도 인용수 순위
1 S. H. Huang, "Dimensionality reduction in automatic knowledge acquisition: a simple greedy search approach," IEEE Trans. Knowledge and Data
2 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.   DOI   ScienceOn
3 J. H. Holland, Adaptation in Natural and Artificial Systems, The University of Michigan Press, Ann Arbour, 1975.
4 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.   DOI
5 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.   DOI   ScienceOn
6 S. K. Oh, W. Pedrycz and H. S. Park, "Hybrid Identification in Fuzzy-Neural Networks," Fuzzy Sets & Systems, Vol. 138, pp. 399-426, 2003.   DOI   ScienceOn
7 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.   DOI   ScienceOn
8 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.   DOI
9 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.
10 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.   DOI   ScienceOn
11 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.   DOI   ScienceOn
12 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.   DOI   ScienceOn
13 K. D. Jong, Are genetic algorithms function optimizers-, In Proc. of PPSN II (Parallel Problem Solving from Nature), Amsterdam, North Holland, 1992.
14 J. A. Cumming and D. A. Wooff, "Dimension reduction via principal variables," Computational Statistics & Data Analysis, Vol. 52, pp. 550-565, 2007.   DOI   ScienceOn
15 W. Pedrycz, "Conditional fuzzy c-means," Pattern Recognition Letter, Vol. 17, pp. 625-631, 1996.   DOI   ScienceOn
16 W. Pedrycz, V. Loia, and S. Senatore, "P-FCM: a proximity-based fuzzy clustering," Fuzzy Sets and Systems, Vol. 148, pp. 21-41, 2004.   DOI   ScienceOn
17 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.
18 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.   DOI   ScienceOn
19 S. P. Lloyd, "Least squares quantization in PCM," IEEE Trans. Information Theory, Vol. 2, pp. 129-137, 1982.
20 J. Park and I. Sandberg, "Universal approximation using radial-basis function networks," Neural Comput., Vol. 3, pp. 246-257, 1991.   DOI
21 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.
22 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.
23 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.   DOI   ScienceOn
24 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   DOI   ScienceOn
25 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.   DOI   ScienceOn
26 W. Pedrycz and K. C. Kwak, "The development of incremental models," IEEE Trans. Fuzzy Systems, Vol. 15, No. 3, pp. 507-518, 2007.   DOI
27 W. Pedrycz, "Fuzzy clustering with a knowledge-based guidance," Pattern Recognition Letter, Vol. 25, pp. 469-480, 2004.   DOI   ScienceOn
28 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.   DOI   ScienceOn