首页|期刊导航|高技术通讯（英文版）|Recognition algorithm of seabed pipeline defect inspection based on dynamic WBF neural networks
高技术通讯（英文版）2005，Vol.11Issue(2)：158-162,5.
Recognition algorithm of seabed pipeline defect inspection based on dynamic WBF neural networks
Recognition algorithm of seabed pipeline defect inspection based on dynamic WBF neural networks
摘要
Abstract
This paper describes a magnetic flux leak (MFL) model of pipeline defect inspection, and presents a recognition algorithm based on dynamic wavelet basis function (WBF) neural network. The dynamic network utilizes multiscale and multiresolution orthogonal wavelet, through signals backwards propagation, has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, high-speed convergence possessing and well approaching feature. The performance applying the algorithm based on the network to predict defect profile from experimental MFL signals is presented.关键词
magnetic flux leak(MFL)/pipeline inspection/WBF neural networks/multiresolutionKey words
magnetic flux leak(MFL)/pipeline inspection/WBF neural networks/multiresolution引用本文复制引用
Jin Tao,Que Peiwen,Tao Zhengshu..Recognition algorithm of seabed pipeline defect inspection based on dynamic WBF neural networks[J].高技术通讯（英文版）,2005,11(2):158-162,5.基金项目
Supported by the High Technology Research and Development Programme of China (No. 2001AA602021). （No. 2001AA602021）
