Universal approximation using radial-basis-function networks
Neural Computation
Approximation and radial-basis-function networks
Neural Computation
A global learing algorithm for a RBF network
Neural Networks
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Neural Networks
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
On the Kernel Widths in Radial-Basis Function Networks
Neural Processing Letters
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Alternating cluster estimation: a new tool for clustering and function approximation
IEEE Transactions on Fuzzy Systems
Generalized weighted conditional fuzzy clustering
IEEE Transactions on Fuzzy Systems
Improved possibilistic C-means clustering algorithms
IEEE Transactions on Fuzzy Systems
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Image interpolation for progressive transmission by using radial basis function networks
IEEE Transactions on Neural Networks
Reformulated radial basis neural networks trained by gradient descent
IEEE Transactions on Neural Networks
Analysis of input-output clustering for determining centers of RBFN
IEEE Transactions on Neural Networks
Approximation of nonlinear systems with radial basis function neural networks
IEEE Transactions on Neural Networks
A new clustering technique for function approximation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
AIKED'09 Proceedings of the 8th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Variable selection in a GPU cluster using delta test
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part I
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The use of Radial Basis Function Neural Networks (RBFNNs) to solve functional approximation problems has been addressed many times in the literature. When designing an RBFNN to approximate a function, the first step consists of the initialization of the centers of the RBFs. This initialization task is very important because the rest of the steps are based on the positions of the centers. Many clustering techniques have been applied for this purpose achieving good results although they were constrained to the clustering problem. The next step of the design of an RBFNN, which is also very important, is the initialization of the radii for each RBF. There are few heuristics that are used for this problem and none of them use the information provided by the output of the function, but only the centers or the input vectors positions are considered. In this paper, a new algorithm to initialize the centers and the radii of an RBFNN is proposed. This algorithm uses the perspective of activation grades for each neuron, placing the centers according to the output of the target function. The radii are initialized using the center's positions and their activation grades so the calculation of the radii also uses the information provided by the output of the target function. As the experiments show, the performance of the new algorithm outperforms other algorithms previously used for this problem.