A clustering algorithm for radial basis function neural network initialization

Neurocomputing

Volumn 77, Issue 1, 2012, Pages 144-155

  Wang, Di ^a   Zeng, Xiao Jun ^a   Keane, John A ^a  

^a UNIVERSITY OF MANCHESTER   (United Kingdom)

Author keywords

Clustering; Fuzzy c mean clustering; Neural network; Radial basis function; Radial basis function neural network

Indexed keywords

APPROXIMATION PERFORMANCE; CLUSTERING; CLUSTERING METHODS; FUZZY C MEAN; HIDDEN NEURONS; INPUT AND OUTPUTS; INPUT COMPLEXITY; RADIAL BASIS FUNCTION NEURAL NETWORKS; RADIAL BASIS FUNCTIONS; REQUIRED PRECISION; SUB-CLUSTERS;

NEURAL NETWORKS; RADIAL BASIS FUNCTION NETWORKS;

CLUSTERING ALGORITHMS;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; FUZZY SYSTEM; GENETIC ALGORITHM; K NEAREST NEIGHBOR; MACHINE LEARNING; NORMAL DISTRIBUTION; OUTPUT CONSTRICTED CLUSTERING; PRIORITY JOURNAL; RADIAL BASED FUNCTION; REGRESSION ANALYSIS; SIMULATION;

EID: 80955131143     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.08.023     Document Type: Article

References (37)

1. Wedge D., Ingram D., McLean D., Clive M., Bandar Z. On global-local artificial neural networks for function approximation. IEEE Trans. Neural Networks 2006, 17(4):942-952.
2. Hong X. A fast identification algorithm for box-cox transformation based radial basis function neural. IEEE Trans. Neural Networks 2006, 17(4):1064-1069.
3. Chen M.S., Manry M.T. Conventional modelling of the multilayer perception using polynomial basis functions. IEEE Trans. Neural Networks 1993, 4(1):164-166.
4. Moody J., Darken C.J. Fast learning in networks of locally tuned processing units. Neural Comput. 1989, 1(2):281-294.
5. Whitehead B.A., Choate T.D. Evolving space-filling curves to distribute radial basis functions over input space. IEEE Trans. Neural Networks 1994, 5(1):15-23.
6. Gonzalez J., Rojas I., H.P., Ortega J. A new clustering technique for function approximation. IEEE Trans. Neural Networks 2002, 13(1):132-142.
7. Gómez-Skarmeta F., Delgado M., Vila M.A. About the use of fuzzy clustering techniques for fuzzy model identification. Fuzzy Sets Syst. 1999, 106(2):179-188.
8. Delgado M., Gomez-Skarmeta A.F., Martin F. A fuzzy clustering-based rapid prototyping for fuzzy rule-based modelling. IEEE Trans. Fuzzy Syst. 1997, 5(2):223-233.
9. L.X. Wang, Training of fuzzy logic systems using nearest neighbourhood clustering, in: Proceedings of the Second IEEE International Conference on Fuzzy Systems, Saint Francisco, CA, US, March 28-April 1, vol. 1, 1993, pp. 13-17.
10. Sugeno M., Yasukawa T. A fuzzy-logic based approach to qualitative modelling. IEEE Trans. Fuzzy Syst. 1993, 1(1):7-31.
11. V.H., Grisales, J.J. Soriano, S. Barato, D.M. Gonzalez, Robust agglomerative clustering algorithm for fuzzy modelling purposes, in: Proceeding of the American Control Conference, Boston, Massachusetts, June 30-July 2, 2004, pp. 1782-1787.
12. R. Tagliaferri, A. Staiano, D. Scala, A supervised fuzzy clustering for radial basis function neural networks training, in: Proceedings of the Joint Ninth IFSA World Congress and Twentieth NAFIPS International Conference, Vancouver, British Columbia, Canada, July 25-28, 2001, pp. 1804-1809.
13. Mao K.Z. RBF neural network center selection based on fisher ratio class separability measure. IEEE Trans. Fuzzy Syst. 2002, 13(5):1211-1217.
14. Juang Chia-Feng, Hsieh Cheng-Da, Taichung A locally recurrent fuzzy neural network with support vector regression for dynamic-system modeling advances. Neural Network Res. Appl. 2010, 18(2):261-273.
15. Sheng Chen, Xiaochen Li, Qiao Cai, Nansai Hu, Haibo He, Yu-Dong Yao, Joseph Mitola, Classification and control of cognitive radios using hierarchical neural network, in: Proceedings of the Seventeenth Mediterranean Conference on Control and Automation, Lecture Notes in Electrical Engineering, vol. 67, Part 4, 347-353, 2010.
16. Rubio José de Jesús, Pacheco Jaime An stable online clustering fuzzy neural network for nonlinear system identification. Neural Comput. Appl. 2008, 18(6):633-641.
17. Kim E., Park M., Ji S. A new approach to fuzzy modelling. IEEE Trans. Fuzzy Syst. 1997, 5(3):328-337.
18. Herrera L.J., Pomares H., Rojas I. New technique for initialization of centres in TSK clustering-based fuzzy systems. Lect. Notes Artif. Intell. 2005, 3571:980-991.
19. Kwak K.-C., Kim D.-H. Adaptive neuro-fuzzy networks with the aid of fuzzy granulation. IEICE Trans. Inf. Syst 2005, E88D(9):2189-2196.
20. Pedrycz W. Constriction fuzzy c-mean algorithm. Pattern Recognition Lett. 1996, 17(6):625-631.
21. Pedrycz W. Conditional fuzzy clustering in the design of radial basis function neural network. IEEE Trans. Neural Networks 2002, 9(4):601-612.
22. Pedrycz W. Linguistic models as a framework of user-centric system modelling. IEEE Trans. Syst. Man Cybern. A 2006, 36(4):727-745.
23. Leski J.M. Generalized weighted conditional fuzzy clustering. IEEE Trans. Fuzzy Syst. 2003, 11(6):709-715.
24. Bezdek J.C. Pattern Recognition with Fuzzy Objective Function Algorithms 1981, Plenum, New York.
25. Zadeh L.A. Fuzzy sets. Inf. Control 1965, 8(3):338-353.
26. Emami M.R., Turksen B., Goldenberg A.A. Development of a systematic methodology of fuzzy modelling. IEEE Trans. Fuzzy Syst. 1998, 6(3):346-361.
27. Tsekouras G.E. On the use of the weighted fuzzy C-means in fuzzy modelling. Adv. Eng. Software 2005, 36(5):287-300.
28. Kim E., Park M., Ji S., Park M. A new approach to fuzzy modelling. IEEE Trans. Fuzzy Syst. 1997, 5(3):328-337.
29. ftp://ics.uci.edu/pub/machine-learning-database/.
30. http://www.spatial-statistics.com.
31. W. Zhao, D.S. Huang, The structure optimization of radial basis probabilistic neural networks based on genetic algorithms, in: Proceedings of the JJCNN'02, Honolulu, HI, USA, 2002, pp. 1086-1091.
32. Guo L., Huang D.-S., Zhao W. Combining genetic optimization with hybrid learning algorithm for radial basis function neural networks. Electron. Lett. 2003, 39(22):1600-1601.
33. Jiang N., Zhao Z., Ren.L. Design of structural modular neural networks with genetic algorithm. Adv. Eng. Software 2003, 34:17-24.
34. Tan S.-C., Lim C Peng Integration of supervised ART-based neural networks with a hybrid genetic algorithm. Soft Comput. 2011, 15:205-219.
35. Suykens J.A.K., Van Gestel T., De Brabanter J., De Moor B., Vandewalle J. Least Squares Support Vector Machines 2002, World Scientific, Singapore.
36. K. De Brabanter, P. Karsmakers, F. Ojeda, C. Alzate, J. De Brabanter, K. Pelckmans, B. De Moor, J. Vandewalle, J.A.K. Suykens, LS-SVMlab Toolbox User's Guide version 1.7, Internal Report 10-146, ESAT-SISTA, K.U.Leuven (Leuven, Belgium), 2010.
37. V. Van Belle, K. Pelckmans, J.A.K. Suykens, S. Van Huffel, Survival SVM: a Practical Scalable Algorithm, in: Proceedings of the Sixteenth European Symposium on Artificial Neural Networks (ESANN2008), Bruges, Belgium, Apr. 2008, pp. 89-94.