Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
A resource-allocating network for function interpolation
Neural Computation
Adaptive neural control with stable learning
Mathematics and Computers in Simulation - Special issue: signal processing and neural networks
An efficient MDL-based construction of RBF networks
Neural Networks
A global learing algorithm for a RBF network
Neural Networks
Adaptive Control
Second Order Derivatives for Network Pruning: Optimal Brain Surgeon
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Fuzzy control of multivariable nonlinear servomechanisms with explicit decoupling scheme
IEEE Transactions on Fuzzy Systems
A differential evolution based incremental training method for RBF networks
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
New Radial Basis Function Neural Network Training for Nonlinear and Nonstationary Signals
Computational Intelligence and Security
IEEE Transactions on Neural Networks
Online training of hierarchical RBF
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
A hierarchical RBF online learning algorithm for real-time 3-D scanner
IEEE Transactions on Neural Networks
Growing RBF networks for function approximation by a DE-Based method
CIS'04 Proceedings of the First international conference on Computational and Information Science
Modified radial basis function network for brain tumor classification
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
The truncated Stieltjes moment problem solved by using kernel density functions
Journal of Computational and Applied Mathematics
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This paper deals with the problem of online adaptation of radial basis function (RBF) neural networks. A new adaptive training method is presented, which is able to modify both the structure of the network (the number of nodes in the hidden layer) and the output weights, as the algorithm proceeds. These adaptation capabilities make the algorithm suitable for modeling dynamical time varying systems, where not only the dynamics but also the operating region changes with time. Therefore, the important issue of extrapolation is faced successfully, but at the same time the algorithm takes care of the size of the network, by deleting the hidden node centers that remain inactive for a long time. The selection of the network centers is based on a fuzzy partition of the input space, which defines a number of fuzzy subspaces. The algorithm considers the centers of the fuzzy subspaces as candidates for becoming hidden node centers and makes the selections, so that at least one center is close enough to each input example. The proposed technique is illustrated through the application to time varying dynamical systems and is compared to other adaptive training methods.