GA-BP neural network model is applied in matching and predicting the production of the gas Wells with 5.1% of the average relative error. It proves th...
利用GA - BP神经网络模型对气井产量进行了拟合和预测，拟合的平均相对误差为5.1%，表明新模型适用于洛带气田的产量递减预测。
Based on the specific geological features of Shaximiao formation, this article used the best subset and GA-BP neural network models to predict the deliverability.
针对沙溪庙组特定的地质特征，本文采用了最佳子集及GA - BP神经网络模型预测产能。
Eventually, the response surfaces composed of the CD main influence factor H1, H2 and limit drawing depth are established by the combination of GA-BP neural network and Latin Hypercube.
最后通过GA - BP神经网络与拉丁超立方抽样法相结合构建了可控拉深筋主要影响因子h1和H2与极限拉深深度之间的响应面。
Combined Genetic Algorithms (ga) and back-propagation neural network (BP), an optimized GA-BP model was established to predict phosphorus content. Some data were chosen to train the network model.
结合遗传算法(GA)和误差反馈型神经网络(BP)，建立了优化的GA - BP神经网络预测模型，预测转炉炼钢过程钢液终点磷含量。
The GA-BP learning algorithm of neural network, the GA learning algorithm, the rule of optimum control including their features were introduced.
给出了作为模型预估器的神经网络GA—BP算法流程及GA 算法实现， 提出了最优控制指标选择原则及控制指标表达式。
The GA-BP learning algorithm of neural network, the GA learning algorithm, the rule of optimum control including their features were introduced.
给出了作为模型预估器的神经网络GA—BP算法流程及GA 算法实现， 提出了最优控制指标选择原则及控制指标表达式。
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