1 |
G. Bugmann, Normalized Gaussian radial basis function networks, Neurocomputing 20 (1998) 97 - 110
DOI
ScienceOn
|
2 |
N. B. Karayiannis, Reformulated radial basis neural networks trained by gradient descent, IEEE Trans. Neural Netw. 10 (3) (1999) 657-671
DOI
ScienceOn
|
3 |
S. M. Loone, G. Irwin, Improving neural network training solutions using regularization, Neurocomputing 37 (2001) 71-90
DOI
ScienceOn
|
4 |
W. Pedrycz and K. C. K wak, 'Linguistic Models as a Framework of User-centric System Modeling', IEEE Trans. on Systems, Man and Cybernetics-A, Vol. 36, No.4, pp. 727-745, 2006
DOI
ScienceOn
|
5 |
M.J.D. Powell, Radial basis functions for multivariable interpolation: a review, in: J.C. Mason, :M.G. Cox (Eds.) , Algorithms for Approximation, Oxford University Press, Oxford, 1987, pp.143-167
|
6 |
Wing W.Y. Ng, Andres Dorado, Daniel S. Yeung, Witold Pedrycz, Ebroul Izquierdo, Image classification with the use of radial basis function neural networks and the minimization of the localized generalization error, Pattern Recognition 40 (2007) 19-32
DOI
ScienceOn
|
7 |
C. G. Looney, Radial basis functional link nets and fuzzy reasoning, Neurocomputing 48 (2002) 489 - 509
DOI
ScienceOn
|
8 |
L. Xu, RBF nets, mixture experts, and Bayesian YingYang learning, Neurocomputing 19 (1998) 223-257
DOI
ScienceOn
|
9 |
Carlos Renjifo, David Barsic, Craig Carmen, Kevin Norman, G.Scott Peacock, Improving radial basis function kernel classification through incremental learning and automatic parameter selection, Neurocomputing 72 (2008) 3-14
DOI
ScienceOn
|
10 |
W. Pedrycz, H.S. Park, S.K. Oh, A granular-oriented development of functional radial basis function neural networks, Neurocomputing 72 (2008) 420-435
DOI
ScienceOn
|
11 |
A. Staiano, R. Tagliaferri, W. Pedrycz, Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering, Neurocomputing 69 (13 - 15) (2006) 1570-1581
DOI
ScienceOn
|
12 |
S. Albrecht, et al., Generalized radial basis function networks for classification and novelty detection: self-organization of optimal Bayesian decision, Neural Networks 13 (2000) 1075-1093
DOI
ScienceOn
|
13 |
L. Ma, A. Wahab, G. S. Ng, S. Erdogan, An experimental study of the extended NRBF regression model and its enhancement for classification problem, Neurocomputing 72 (2008) 458-470
DOI
ScienceOn
|
14 |
S. Mitra, J. Basak, FRBF: a fuzzy radial basis function network, Neural Comput. Appl. 10 (2001) 244-252
DOI
ScienceOn
|
15 |
A. Staiano, R. Tagliaferri, W. Pedrycz, Improving RBF networks performance in regression tasks by means of a supervised fuzzy clustering, Neurocomputing 69 (13-15) (2006) 1570-1581
DOI
ScienceOn
|
16 |
W. Pedrycz, Conditional fuzzy clustering in the design of radial basis function neural networks, IEEE Trans. Neural Netw. 9 (4) (1998) 601 - 612
DOI
ScienceOn
|
17 |
L. Marcelino, S. Ignacio, P. Carlos, A new EM-based training algorithm for RBF networks, Neural Networks 16 (1) (2003) 69-77
DOI
ScienceOn
|