Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Fast learning in networks of locally-tuned processing units
Neural Computation
Hybrid Learning Enhancement of RBF Network Based on Particle Swarm Optimization
ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Improving performance of radial basis function network based with particle swarm optimization
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Designing RBFNNs using prototype selection
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Dimensionality reduction for evolving RBF networks with particle swarms
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
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When a radial-basis function neural network (RBFNN) is used for pattern classification, the problem involves designing the topology of RBFNN and also its centers and widths. In this paper, we present a particle swarm optimization (PSO) learning algorithm to automate the design of RBF networks, to solve pattern classification problems. Simulation results for benchmark problems in the pattern classification area show that the PSO-RBF outperforms two other learning algorithms in terms of network size and generalization performance.