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- W2378087914 abstract "A Predictive reservoir model with the self-adoption and complicated nonlinear property is set up. Because Multi-layer Feed Forward Neural Networks BP Algorithm exists weakness of getting bogged down in the local optima, stronger robustness and global convergence of PSO Algorithm, this research makes use of the particle swarm optimization (PSO) to improve the neural network, then, on the basis of the Luodai gas field in Sichuan Province, by the computation methods of PSO of the neural network, the reservoir characters(such as porosity, permeability) is forecasted, also the precision is tested and is compared with traditional computation methods of BP and LMBP, by which a obvious geography efficiency superior to traditional explanation methods is obtained, the shortcoming base from BP and LMBP algorithm are effectively overcome." @default.
- W2378087914 created "2016-06-24" @default.
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- W2378087914 date "2007-01-01" @default.
- W2378087914 modified "2023-09-23" @default.
- W2378087914 title "RESERVOIR PARAMETER PREDICTION OF NEURAL NETWORK BASED ON PARTICLE SWARM OPTIMIZATION" @default.
- W2378087914 hasPublicationYear "2007" @default.
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