Abstract
Modelling capabilities of Radial Basis Function Neural Networks (RBFNNs) are very dependent on four main factors: the number of neurons, the central location of each neuron, their associated weights and their widths (radii). In order to model surfaces defined, for example, as y = f(x,z), it is common to use tri-dimensional gaussian functions with centres in the (X,Z) domain. In this scenario, it is very useful to have visual environments where the user can interact with every radial basis function, modify them, inserting and removing them, thus visually attaining an initial configuration as similar as possible to the surface to be approximated. In this way, the user (the novice researcher) can learn how every factor affects the approximation capability of the network, thus gaining important knowledge about how algorithms proposed in the literature tend to improve the approximation accuracy. This paper presents a didactic tool we have developed to facilitate the understanding of surface modelling concepts with ANNs in general and of RBFNNs in particular, with the aid of a virtual environment.
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López, M.Á. et al. (2007). Surface Modelling with Radial Basis Functions Neural Networks Using Virtual Environments. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_21
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DOI: https://doi.org/10.1007/978-3-540-73007-1_21
Publisher Name: Springer, Berlin, Heidelberg
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