Extensions of a theory of networks for approximation and learning: outliers and negative examples
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
IEEE Transactions on Computers
A general regression neural network
IEEE Transactions on Neural Networks
Genetic algorithms for MLP neural network parameters optimization
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
An optimizing BP neural network algorithm based on genetic algorithm
Artificial Intelligence Review
Ontology alignment using artificial neural network for large-scale ontologies
International Journal of Metadata, Semantics and Ontologies
Artificial Intelligence Review
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Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.