Based on the basic principles of BP artificial neural network model and the fundamental law of water and sediment yield in a river basin,a BP artificial neural network model is developed by using observed data,with rainfall conditions serving as affecting factors.
First,some parameters to reflect reservoir productivity are extracted in terms of their geology and log characteristics. Considering the nonlinear relation between parameters and production capability and the varying rule of these data,BP artificial neural network is adopted to create the predicting mathematical model. And log data of many wells in LN oilfield are processed.
In this paper, the water level and runoff is forecasted by BP ANN between hydrometric stations and we get conclusions: Under the prediction model in this paper, the difference of forecast accuracy is obvious between the combinations of hydrological series, which is associated with the linear relation between the data, and the linear relation more clear, the prediction much better.
In this paper, a test on JIN66 well by method of BP ANN to identify well lithology has been made, and a method to improving convergence speed for BP neural network been explored.
The relation between the strong earthquake occurrences in China and in the world has been studied using the BP neuro-network The results of tests have shown that rather good prediction results can be reached by this method
This paper deals with the study of a water quality forecast model through application of BP neural network technique and GUI (Graphical User Interfaces) function of MATLAB at Yuqiao reservoir in Tianjin.
Also it has been demonstrated how to solve the group path integral equation with Back Propagation (BP) neural network, and given the computation results with this method for a parabolic model.
Based on the idea of nonlinear prediction of phase space reconstruction, this paper presented a time delay BP neural network model, whose generalization capability was improved by Bayesian regularization.
Finally, by choosing main factors impacting on the natural runoff, one error back-propagation (BP) artificial neural network (ANN) model has been set up, and the impact of human activities on natural runoff reduction in the YRB is simulated.
The results show that the accuracy of prediction of SVC model was higher than those of back propagation artificial neural network (BP ANN), K-nearest neighbor (KNN) and Fisher methods.
It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation.
First, we present an improved version of our previous positioning back propagation (BP) ANN multi-level perceptron (MLP) model that further improves positioning accuracy.