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- W2384622101 abstract "In order to accommodate non-uniformity,non-linearity,and uncertainty and improve forecast accuracy of a complex oil and gas reservoir,this paper proposes a hybrid self-generated training algorithm of RBF neural networks,which combines multi-encoding particle swarm optimization and back propagation(MPSO-BP) algorithms.In the algorithm,every particle consists of binary and real parts,which correspond to the coding of the number of hidden units and associated parameters(i.e.,widths,centers and weights),respectively.Furthermore,a special fitness function is introduced to ensure accuracy of the algorithm and to improve self-adaptability and generality of the RBF neural networks.Applications in oil reservoir prediction show that the proposed method has fewer hidden nodes,higher accuracy,and better generality compared with other methods." @default.
- W2384622101 created "2016-06-24" @default.
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- W2384622101 date "2011-01-01" @default.
- W2384622101 modified "2023-09-28" @default.
- W2384622101 title "An MPSO-BP-RBF Neural Network Model and its Application in Oil Reservoir Forecasting" @default.
- W2384622101 hasPublicationYear "2011" @default.
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