Studying possibility in a clustering algorithm for RBFNN design for function approximation

Neural Computing and Applications

Volumn 17, Issue 1, 2008, Pages 75-89

  Guillen A ^a   Pomares, H ^b   Rojas, I ^b   Gonzalez J ^b   Herrera, L J ^b   Rojas, F ^b   Valenzuela, O ^b  

^a University of Jaán   (Spain)

^b UNIVERSITY OF GRANADA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 36549081039     PISSN: 09410643     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00521-007-0134-6     Document Type: Article

References (21)

1. Barni M, Capellini V, Mecocci A (1996) Comments on 'A possibilistic approach to clustering'. IEEE Trans Fuzzy Syst 4:393-396
2. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum press, New York
3. Cherkassky V, Lari-Najafi H (1991) Constrained topological mapping for nonparametric regression analysis. Neural Netw 4(1):27-40
4. Friedman JH (1991) Multivariate adaptive regression splines (with discussion). Ann Stat 19:1-141
5. Friedman JH (1981) Projection pursuit regression. J Am Stat Assoc 76:817-823
6. González J, Rojas I, Pomares H, Ortega J, Prieto A (2002) A new clustering technique for function aproximation. IEEE Trans Neural Netw 13(1):132-142
7. Guillén A, Rojas I, González J, Pomares H, Herrera LJ, Valenzuela O, Prieto A (2005) Improving clustering technique for functional approximation problem using fuzzy logic: ICFA algorithm. Lect Notes Comput Sci 3512:272-280
8. Hartigan JA (1975) Clustering algorithms. Wiley, New York
9. Karayiannis NB, Mi GW (1997) Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques. IEEE Trans Neural Netw 8:1492-1506
10. Krishnapuram R, Keller JM (1993) A possibilistic approach to clustering. IEEE Trans Fuzzy Syst 1(2):98-110
11. Moody J, Darken CJ (1989) Fast learning in networks of locally-tunned processing units. Neural Comput 1(2):281-294
12. Pal NR, Pal K, Bezdek JC (1997) A mixed C-means clustering model. In: Proceedings of the 6th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'97), vol 1, Barcelona, pp 11-21
13. Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517-530
14. Park J, Sandberg JW (1991) Universal approximation using radial basis functions network. Neural Comput 3:246-257
15. Poggio T, Girosi F (1990) Networks for approximation and learning. In: Proceedings of the IEEE, vol 78, pp 1481-1497
16. Pomares H (2000) Nueva metodología para el diseño automático de sistemas difusos. PhD dissertation, University of Granada, Spain
17. Rojas I, Anguita M, Prieto A, Valenzuela O (1998) Analysis of the operators involved in the definition of the implication functions and in the fuzzy inference proccess. Int J Approx Reason 19:367-389
18. Blake CL, Hettich S, Merz CJ (1998) UCI repository of machine learning databases
19. Timm H, Kruse R (2002) A modification to improve possibilistic fuzzy cluster analysis. In: Proceedings of the 2002 IEEE International Conference on Fuzzy Systems, vol 2, pp 1460-1465
20. Zhang J, Leung Y (2004) Improved possibilistic C-means clustering algorithms. IEEE Trans Fuzzy Syst 12:209-217
21. Zhu Q, Cai Y, Liu L (1999) A global learning algorithm for a RBF network. Neural Netw 12:527-540