喻西崇, 赵金洲, 胡永全, 郭建春, 邬亚玲. 人工神经网络预测注水腐蚀管道的剩余寿命[J]. 油气储运, 2002, 21(6): 11-14. DOI: 10.6047/j.issn.1000-8241.2002.06.004
引用本文: 喻西崇, 赵金洲, 胡永全, 郭建春, 邬亚玲. 人工神经网络预测注水腐蚀管道的剩余寿命[J]. 油气储运, 2002, 21(6): 11-14. DOI: 10.6047/j.issn.1000-8241.2002.06.004
YU Xichong, ZHAO Jinzhou, . Predicting the Residual Life of Injecting Water Pipeline with the Artificial Neural Network[J]. Oil & Gas Storage and Transportation, 2002, 21(6): 11-14. DOI: 10.6047/j.issn.1000-8241.2002.06.004
Citation: YU Xichong, ZHAO Jinzhou, . Predicting the Residual Life of Injecting Water Pipeline with the Artificial Neural Network[J]. Oil & Gas Storage and Transportation, 2002, 21(6): 11-14. DOI: 10.6047/j.issn.1000-8241.2002.06.004

人工神经网络预测注水腐蚀管道的剩余寿命

Predicting the Residual Life of Injecting Water Pipeline with the Artificial Neural Network

  • 摘要: 利用人工神经网络的自适应、自组织学习能力, 通过对训练样本集的学习, 使用传统的CVDA—84规范、传统的BP神经网络、改进的Rumelhart和MBP神经网络, 对注水管道的剩余寿命进行了预测。结果表明, CVDA—84规范偏保守, 采用BP以及改进的BP神经网络预测的剩余寿命和观测值基本一致。但采用BP人工神经网络预测时, 迭代次数比CVDA多得多。采用改进的Rumelhart和MBP神经网络能有效地提高预测速度, 改善网络的收敛性, 并且使预测精度有所提高。

     

    Abstract: In this paper, self-adapting and self-organizing study ability of the artificial neural network is used to predict the residual life of injecting water pipeline. By means of field example, four different predicting methods, for example, CVDA—84 standard, conventional BP neural network, improved Rumelhart and MBP neural network, are adopted to predict residual life of injecting water pipeline. The predicted results show that CVDA—84 standard is somewhat conservative, BP neural network, the improved Rumelhart and MBP neural network methods are good agreement with field data, while iterate number is great. The improved Rumelhart and MBP neural network methods can overcome the shortcomings exsited in the rest methods.

     

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