基于RBF神经网络的输油管道安全停输时间预测

针对影响海底输油管道停输的因素复杂,难以对管道安全停输时间做出准确判断的问题,提出了海底输油管道安全停输时间预测的径向基函数(RBF)神经网络模型,综合考虑了各因素对输油管道安全停输的影响。以实测数据为基础,训练网络并验证了模型的预测准确性。研究结果表明,径向基函数神经网络预测模型对训练样本的拟合精度和对验证样本的仿真精度分别达到98.40%和97.33%,可对海底输油管道安全停输时间进行有效预测,为海底输油管道的安全输送提供重要依据。

Prediction of Safe Shutdown Time of Oil Pipeline Based on RBF Neural Network

Considering the difficulty in the estimation of safe shutdown time of oil pipeline due to the complex safe shutdown of submarine oil pipeline influencing factors,the radial basis function neural network model was proposed for predicting safe shutdown time of submarine oil pipeline and the various influence factors on safe shutdown of oil pipeline were analyzed.The model was validated on the basis of actual data and the network was trained and the accuracy was verified.The results show that the fitting and simulation precision for training and testing samples is 98.40% and 97.33%,respectively.So the safe shutdown time of submarine oil pipeline can be predicted validly,and the important basis of safe transportation of submarine oil pipeline was provided.