Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Fast learning in networks of locally-tuned processing units
Neural Computation
Studying the convergence of the CFA algorithm
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
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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.