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- W2388846787 abstract "In order to increase the hit rate of the BOF endpoint,a static model of converter steelmaking was established using radial basis function neural network.Quantum-behaved particle swarm optimization has better global search capability,but gradient descent method has better local search capability.To be able to play the advantages of the two algorithms,a combination of training methods was proposed,to train the radial basis function neural network.Stimulation was done based on the historical data of a steel plant,so as to compare the combination method and non-combination methods.The results indicate that the analysis improves prediction accuracy of the converter end C content and temperature." @default.
- W2388846787 created "2016-06-24" @default.
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- W2388846787 date "2011-01-01" @default.
- W2388846787 modified "2023-09-25" @default.
- W2388846787 title "A Static Model of Converter Steelmaking Using RBFNN Based on Combined Methods" @default.
- W2388846787 hasPublicationYear "2011" @default.
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