A resource-allocating network for function interpolation
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
An efficient MDL-based construction of RBF networks
Neural Networks
On Information-Theoretic Measures of Attribute Importance
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Regularization in the selection of radial basis function centers
Neural Computation
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Learning without local minima in radial basis function networks
IEEE Transactions on Neural Networks
Training RBF neural networks with PSO and improved subtractive clustering algorithms
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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This paper proposes a new adaptive learning algorithm of network structure aimed at improving RBF network generalization ability. The algorithm determines the initial number and center vectors of network hidden units by using forward selective clustering algorithm with decaying radius, and then adjusts them by using cluster sample transform algorithm based on impurity and variance and gets the final center vectors. The determination of widths of hidden units considers both the dispersivity of inner samples and the distance between clusters. Thus we get the final hidden structure. After determining the hidden structure, the back-propagation algorithm is used to train the weights between the hidden layer and output layer. The experiment of two spirals problem proves that our algorithm has higher generalization ability indeed.