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ON RADIAL BASIS FUNCTION NEURAL NETWORKS | ||
| journal of al-qadisiyah for pure science(quarterly) | ||
| Article 1, Volume 7, Issue 3, 2007, Pages 278-300 PDF (0 K) | ||
| Authors | ||
| ; Q.H. Eqhaar | ||
| Abstract | ||
| Artificial Neural Networks (ANN's) are largely used in applications involving classification or functions approximation. It has been proved that several classes of ANN such as Multilayer Radial-Basis Function Networks (RBFN) are universal function approximators . Therefore, they are widely used for function approximation . In this paper ,we examine the similarities and differences between RBFNNs compare the performance of learning with each representation applied to the interpolation problem. Nonetheless, this paper should help the reader to understand which basis function and which efficient method should be employed for particular reconstruction problem. It should also encourage the reader to consult the literature pointed out in the bibliography for further studying. | ||
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