LetterSecond derivative dependent placement of RBF centers
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Cited by (15)
An improved radial basis function neural network for object image retrieval
2015, NeurocomputingCitation Excerpt :This makes particles find the global optimal solution. Although the setting of the basis function centers has been highly addressed by the previous works on RBFNN learning [25–27], the learning of the basis function widths has not been much studied. The existing previous works discussed the effect of widths of radial basis functions on performances of classification and function approximation [4,28,29].
Generalized multiscale radial basis function networks
2007, Neural NetworksCitation Excerpt :Unlike the separate learning procedure, combined learning approaches, which are often implemented by means of supervised learning algorithms, aim to simultaneously estimate all the three kinds of unknown parameters by performing appropriate nonlinear optimization techniques including gradient descent search (Karayiannis, 1999; McLoone, Brown, Irwin, & Lightbody, 1998), expectation-maximization (EM) estimation (Lazaro, Santamaria, & Pantaleon, 2003), and evolutionary algorithms (Billings & Zheng, 1995; Gonzalez et al., 2003; Whitehead & Choate, 1996). While several efficient algorithms have been introduced to determine kernel centres (Billings & Chen, 1998; Haykin, 1999; Moody & Darken, 1989; Orr, 1995; Sanchez, 1995; Schwenker et al., 2001), few algorithms are available to effectively determine the kernel widths of the basis functions in the network for the general purpose of nonlinear system identification problems. In fact, the optimization of kernels is often coupled with the estimation of other parameters in most existing learning strategies.
Radial basis functional link nets and fuzzy reasoning
2002, NeurocomputingOn the design of a class of neural networks
1996, Journal of Network and Computer ApplicationsRobustization of a learning method for RBF networks
1995, Neurocomputing