摘要
用BP神经网络分析评判管道的腐蚀类型,可以避开寻找各种因素对腐蚀类型影响规律的难题,方便准确地分析评判出管道的腐蚀类型,但是传统的BP神经网络存在收敛速度较慢和容易陷入局部极小点两个问题,为此文章提出了将传统的BP神经网络与共轭梯度优化算法相结合,以优化网络权值和阈值的计算,同时确定了相应的计算方法。将改进后的BP神经网络应用到管道腐蚀类型的评判中,取得了良好的效果。计算结果表明,改进后的BP神经网络具有更好的学习能力,可以在更少的迭代次数和时间内,得到高精度的输出结果。
The analysis and judgment of pipeline corrosion patterns with improve d BP neural network can overcome difficulties of searching for various factors a ffecting corrosion patterns. The pipeline corrosion patterns can be analyzed and judged conveniently and accurately. But conventional BP neural network has two problems, i.e. lower convergent speed and partial minimum point. This paper com bines conventional BP neural network with conjugated gradient optimum algorithm to optimize the calculations of network weighting value and threshold value,and furthermore,it offers the relevant calculation method. The application of improv ed BP neural network to pipeline corrosion pattern judgment achieves good result s. The network possesses better study ability and gives more accurate output wit h less iterative frequency and less time.
出处
《石油工程建设》
2005年第3期4-7,共4页
Petroleum Engineering Construction
关键词
BP神经网络
共轭梯度优化算法
管道
腐蚀类型
评判
BP neural network
conjugated gradient optimum algorithm
pipeline
corrosion pattern
judgment
