Radial basis functions for multivariable interpolation: a review
Algorithms for approximation
A resource-allocating network for function interpolation
Neural Computation
Neural Computation
A function estimation approach to sequential learning with neural networks
Neural Computation
An efficient MDL-based construction of RBF networks
Neural Networks
Journal of Global Optimization
An Algorithm for Data-Driven Bandwidth Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
SSA, SVD, QR-cp, and RBF Model Reduction
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Automatic basis selection techniques for RBF networks
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
Fast learning in networks of locally-tuned processing units
Neural Computation
Information theoretic learning with adaptive kernels
Signal Processing
IEEE Transactions on Neural Networks
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
Fast orthogonal forward selection algorithm for feature subset selection
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
IEEE Transactions on Neural Networks
An information criterion for optimal neural network selection
IEEE Transactions on Neural Networks
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
IEEE Transactions on Neural Networks
Granular support vector machine based on mixed measure
Neurocomputing
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The Orthogonal Least Squares (OLS) algorithm has been extensively used in basis selection for RBF networks, but it is unable to perform model selection automatically because the tolerance @r must be specified manually. This introduces noise and it is difficult to implement in the parametric complexity of real-time system. Therefore, a generic criterion that detects the optimum number of its basis functions is proposed. In this paper, not only the Bayesian Information Criterion (BIC) method, used for fitness calculation, is incorporated into the basis function selection process of the OLS algorithm for assigning its appropriate number, but also a new method is developed to optimize the widths of the Gaussian functions in order to improve the generalization performance. The augmented algorithm is employed to the Radial Basis Function Neural Networks (RBFNN) for known and unknown noise nonlinear dynamic systems and its performance is compared with the standard OLS; experimental results show that both the efficacy of BIC for fitness calculation and the importance of proper choice of basis function widths are significant.