Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Theory of Networks for Approximation and Learning
A Theory of Networks for Approximation and Learning
Testing Error Estimates for Regularization and Radial Function Networks
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
Learning methods for radial basis function networks
Future Generation Computer Systems
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In this work we propose two hybrid algorithms combining evolutionary search with optimization algorithms One algorithm memetically combines global evolution with gradient descent local search, while the other is a two-step procedure combining linear optimization with evolutionary search It is shown that these algorithms typically produce smaller local unit networks with performance similar to theoretically sound but large regularization networks.