Approximation and radial-basis-function networks
Neural Computation
Fast learning in networks of locally-tuned processing units
Neural Computation
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Sensitivity analysis of multilayer perceptron to input and weight perturbations
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
Sensitivity of feedforward neural networks to weight errors
IEEE Transactions on Neural Networks
Orthogonal least squares learning algorithm for radial basis function networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
The selection of weight accuracies for Madalines
IEEE Transactions on Neural Networks
Product Demand Forecasting with a Novel Fuzzy CMAC
Neural Processing Letters
An online Bayesian Ying-Yang learning applied to fuzzy CMAC
Neurocomputing
A novel pruning algorithm for self-organizing neural network
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy CMAC with incremental Bayesian Ying-Yang learning and dynamic rule construction
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A hybrid radial basis function and data envelopment analysis neural network for classification
Computers and Operations Research
Particle swarm optimization aided orthogonal forward regression for unified data modeling
IEEE Transactions on Evolutionary Computation
KCMAC-BYY: Kernel CMAC using Bayesian Ying-Yang learning
Neurocomputing
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Conventionally, a radial basis function (RBF) network is constructed by obtaining cluster centers of basis function by maximum likelihood learning. This paper proposes a novel learning algorithm for the construction of radial basis function using sensitivity analysis. In training, the number of hidden neurons and the centers of their radial basis functions are determined by the maximization of the output's sensitivity to the training data. In classification, the minimal number of such hidden neurons with the maximal sensitivity will be the most generalizable to unknown data. Our experimental results show that our proposed sensitivity-based RBF classifier outperforms the conventional RBFs and is as accurate as support vector machine (SVM). Hence, sensitivity analysis is expected to be a new alternative way to the construction of RBF networks.