There was a long training time for the norm BP neural network for GPS Height fitting,and easily converging to local minimum ***,introduced momentum and adaptive learning rate algorithm to improve the norm BP neural ne...
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There was a long training time for the norm BP neural network for GPS Height fitting,and easily converging to local minimum ***,introduced momentum and adaptive learning rate algorithm to improve the norm BP neural network for resolving the problem of the training and *** with the standard neural network,and calculating by a regional elevation control point coordinates,additionalmomentum adaptive neural network algorithm accuracy of GPS height conversion was much higher and more stable,and the convergence was much faster.
Coalbed methane (CBM) productive efficiency and coal mine disasters such as gas outbursts and water inrush are closely correlated with coal seam permeability. Effective prediction of coal seam permeability can provide...
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Coalbed methane (CBM) productive efficiency and coal mine disasters such as gas outbursts and water inrush are closely correlated with coal seam permeability. Effective prediction of coal seam permeability can provide guidance for CBM production and prevention of coal mine disasters. In this research, a hybrid neural network prediction model integrating a genetic algorithm, an adaptive boosting algorithm, and a back propagation neural network was developed to predict coal seam permeability. additionalmomentum and variable learning rate algorithms were used to improve the learning rate and accuracy of the model, and the model structure was optimized, including the number of hidden layer nodes and the transfer function. The input parameters of the prediction model included gas pressure, compressive strength, reservoir temperature, and effective stress. The corresponding output parameter was coal seam permeability. The correlation between the parameters was calculated. additionally, a comparative analysis between the proposed prediction model and four other prediction models was carried out to demonstrate the advantages of the proposed model. The results indicated that the correlations between compressive strength, gas pressure, reserve temperature, effective stress, and coal seam permeability were 0.334, -0.148, -0.406, and -0.785, respectively. The proposed prediction model had high accuracy compared with the other prediction models, and its coefficient of determination and root mean squared error were 0.999 and 0.021. Thus, the model can predict coal seam permeability more accurately.
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