摘要
钢板表面缺陷自动分类系统是近年来的研究热点之一。为了解决多无损检测源数据融合法检测缺陷尺寸的问题 ,给出了一种适用于多传感器数据融合的模糊神经网络模型 ,并对该模型的结构特点及实现进行了详细讨论。初步试验结果表明 ,此模型在一定程度上解决了从不同无损检测源所测值对钢板缺陷进行有效定量分析这一问题。它在无损检测中的应用表明该模型解决了传统模型中存在一些问题 。
Auto classification system of steel plate′s surface defects is one of the hottest issues in recent years. To solve the problem of defects size inspection through different NDT sources data fusion, a new fuzzy neural networks model adapted to multisensor data fusion is presented and the realization of this model and its characteristics are discussed in detail. The application of this model on the inspection of surface defect sizes shows that a quantitative method for determining the actual defect size is successfully developed to make full use of the measured defects sizes from different NDT sources. The preliminary result shows that the performance of this model can solve some problems of traditional models as well as this model can also be used in many other fields.
出处
《计算机测量与控制》
CSCD
2003年第1期14-16,19,共4页
Computer Measurement &Control
关键词
钢板
无损检测
模糊神经网络
参数识别
模式识别
fuzzy neural networks
nondestructive testing
parameter recognition
steel plate
