On functional approximation with normalized Gaussian units
Neural Computation
Interference cancellation using radial basis function networks
Signal Processing
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Comparison of adaptive methods for function estimation from samples
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Shape-adaptive radial basis functions
IEEE Transactions on Neural Networks
RBF Neural Networks, Multiobjective Optimization and Time Series Forecasting
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Expert Mutation Operators for the Evolution of Radial Basis Function Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Connectionist Models of Neurons, Learning Processes and Artificial Intelligence-Part I
Hybridization of intelligent techniques and ARIMA models for time series prediction
Fuzzy Sets and Systems
Applications in Bio-informatics and Biomedical Engineering
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
On Selecting the Best Pre-processing Method for Affymetrix Genechips
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Efficient Optimization of the Parameters of LS-SVM for Regression versus Cross-Validation Error
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
The antiquadrupolar phase of the biquadratic neural network
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
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The main architectures, learning abilities and applications of radial basis function (RBF) neural networks are well documented. However, to the best of our knowledge, no in-depth analyses have been carried out into the influence on the behaviour of the neural network arising from the use of different alternatives for the design of an RBF (different non-linear functions, distances, number of neurons, structures, etc.). Thus, as a complement to the existing intuitive knowledge, it is necessary to have a more precise understanding of the significance of the different alternatives. In the present contribution, the relevance and relative importance of the parameters involved in such a design are investigated by using a statistical tool, the ANalysis Of the VAriance (ANOVA). In order to obtain results that are widely applicable, various problems of classification, functional approximation and time series estimation are analyzed. Conclusions are drawn regarding the whole set.