What size net gives valid generalization?
Neural Computation
A neural root finder of polynomials based on root moments
Neural Computation
Neural Computing and Applications
A fuzzy neighborhood-based training algorithm for feedforward neural networks
Neural Computing and Applications
A new modified hybrid learning algorithm for feedforward neural networks
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Magnified gradient function with deterministic weight modification in adaptive learning
IEEE Transactions on Neural Networks
An efficient constrained training algorithm for feedforward networks
IEEE Transactions on Neural Networks
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In this paper, a constrained learning algorithm is proposed for function approximation. The algorithm incorporates constraints into single hidden layered feedforward neural networks from the a priori information of the approximated function. The activation functions of the hidden neurons are specific polynomial functions based on Taylor series expansions, and the connection weight constraints are obtained from the second-order derivative information of the approximated function. The new algorithm has been shown by experimental results to have better generalization performance than other traditional learning ones.