BP神经网络精度估计及其在海洋油气资源预测中的应用

在对BP算法进行深入分析的基础上,将测量数据处理与误差理论中的精度评定方法应用到BP神经网络的精度估计中,通过分别计算BP神经网络学习训练过程及预测过程的输出层中误差,实现对神经网络模型的精度评定。最后以海洋油气资源预测为例,结合实测资料建立了BP神经网络预测模型并分别进行了学习训练过程及预测过程的精度评定,以期为神经网络模型结构的优化设计提供有效参考,为提高神经网络模型的适用性提供科学依据。

Precision estimation of BP neural network and its application in ocean oil and gas resources prediction

Based on an intensive analysis of the back-propagation (BP) algorithm, we use the accuracy estimation method for surveying error data processing and the error theory to estimate the precision of a BP neural network. We calculate the mean square errors of the output layer in the learning and prediction processes of the BP neural network, respectively, to evaluate the accuracy of the neural network model. Lastly, taking ocean oil and gas resources prediction as an example, we use measured data to establish a BP neural network prediction model and calculate the precision of the learning and prediction processes of the BP neural network. The results indicate that the proposed precision analysis can provide an effective reference for the optical design of the BP network structure and provide scientific basis for improving the applicability of the neural network model.