180 related articles for article (PubMed ID: 18238063)
1. On the construction and training of reformulated radial basis function neural networks.
Karayiannis NB; Randolph-Gips MM
IEEE Trans Neural Netw; 2003; 14(4):835-46. PubMed ID: 18238063
[TBL] [Abstract][Full Text] [Related]
2. Reformulated radial basis neural networks trained by gradient descent.
Karayiannis NB
IEEE Trans Neural Netw; 1999; 10(3):657-71. PubMed ID: 18252566
[TBL] [Abstract][Full Text] [Related]
3. Training reformulated radial basis function neural networks capable of identifying uncertainty in data classification.
Karayiannis NB; Xiong Y
IEEE Trans Neural Netw; 2006 Sep; 17(5):1222-34. PubMed ID: 17001983
[TBL] [Abstract][Full Text] [Related]
4. Growing radial basis neural networks: merging supervised and unsupervised learning with network growth techniques.
Karayiannis NB; Mi GW
IEEE Trans Neural Netw; 1997; 8(6):1492-506. PubMed ID: 18255750
[TBL] [Abstract][Full Text] [Related]
5. Sensitivity analysis applied to the construction of radial basis function networks.
Shi D; Yeung DS; Gao J
Neural Netw; 2005 Sep; 18(7):951-7. PubMed ID: 15939573
[TBL] [Abstract][Full Text] [Related]
6. A growing and pruning sequential learning algorithm of hyper basis function neural network for function approximation.
Vuković N; Miljković Z
Neural Netw; 2013 Oct; 46():210-26. PubMed ID: 23811384
[TBL] [Abstract][Full Text] [Related]
7. Natural gradient learning algorithms for RBF networks.
Zhao J; Wei H; Zhang C; Li W; Guo W; Zhang K
Neural Comput; 2015 Feb; 27(2):481-505. PubMed ID: 25380332
[TBL] [Abstract][Full Text] [Related]
8. Data classification with radial basis function networks based on a novel kernel density estimation algorithm.
Oyang YJ; Hwang SC; Ou YY; Chen CY; Chen ZW
IEEE Trans Neural Netw; 2005 Jan; 16(1):225-36. PubMed ID: 15732402
[TBL] [Abstract][Full Text] [Related]
9. Automatic determination of radial basis functions: an immunity-based approach.
de Castro LN; Von Zuben FJ
Int J Neural Syst; 2001 Dec; 11(6):523-35. PubMed ID: 11852437
[TBL] [Abstract][Full Text] [Related]
10. Radial basis function networks with linear interval regression weights for symbolic interval data.
Su SF; Chuang CC; Tao CW; Jeng JT; Hsiao CC
IEEE Trans Syst Man Cybern B Cybern; 2012 Feb; 42(1):69-80. PubMed ID: 21859627
[TBL] [Abstract][Full Text] [Related]
11. A new formulation for feedforward neural networks.
Razavi S; Tolson BA
IEEE Trans Neural Netw; 2011 Oct; 22(10):1588-98. PubMed ID: 21859600
[TBL] [Abstract][Full Text] [Related]
12. Robust radial basis function neural networks.
Lee CC; Chung PC; Tsai JR; Chang CI
IEEE Trans Syst Man Cybern B Cybern; 1999; 29(6):674-85. PubMed ID: 18252348
[TBL] [Abstract][Full Text] [Related]
13. A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation.
Huang GB; Saratchandran P; Sundararajan N
IEEE Trans Neural Netw; 2005 Jan; 16(1):57-67. PubMed ID: 15732389
[TBL] [Abstract][Full Text] [Related]
14. Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on Gaussian mixtures, three-layer nets and ME-RBF-SVM models.
Xu L
Int J Neural Syst; 2001 Feb; 11(1):43-69. PubMed ID: 11310554
[TBL] [Abstract][Full Text] [Related]
15. A hybrid linear/nonlinear training algorithm for feedforward neural networks.
McLoone S; Brown MD; Irwin G; Lightbody A
IEEE Trans Neural Netw; 1998; 9(4):669-84. PubMed ID: 18252490
[TBL] [Abstract][Full Text] [Related]
16. Kernel orthonormalization in radial basis function neural networks.
Kaminski W; Strumillo P
IEEE Trans Neural Netw; 1997; 8(5):1177-83. PubMed ID: 18255719
[TBL] [Abstract][Full Text] [Related]
17. Numerical solution of elliptic partial differential equation using radial basis function neural networks.
Jianyu L; Siwei L; Yingjian Q; Yaping H
Neural Netw; 2003; 16(5-6):729-34. PubMed ID: 12850028
[TBL] [Abstract][Full Text] [Related]
18. Constructive approximation to multivariate function by decay RBF neural network.
Hou M; Han X
IEEE Trans Neural Netw; 2010 Sep; 21(9):1517-23. PubMed ID: 20693108
[TBL] [Abstract][Full Text] [Related]
19. An axiomatic approach to soft learning vector quantization and clustering.
Karayiannis NB
IEEE Trans Neural Netw; 1999; 10(5):1153-65. PubMed ID: 18252616
[TBL] [Abstract][Full Text] [Related]
20. An ART-based construction of RBF networks.
Lee SJ; Hou CL
IEEE Trans Neural Netw; 2002; 13(6):1308-21. PubMed ID: 18244529
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]