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An adaptive inspection-method for industrial welding seam based on PCA algorithm and the modification of BP ANN

机译:基于PCA算法和BP神经网络改进的工业焊缝自适应检测方法。

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The paper adopts an adaptive momentum adjustments algorithm to standardize various lighting parameters, so as to improve its adaptive capacity for the environment. By PCA algorithm, the original data space is compressed into an eigenvalue pattern space, then the data will gather in a lesser effective eigenvalue space. Using the modification of BP ANN to classify the data pattern space, it can realize the system's self-teaching function, and improve the correct-inspection rate and real-time performance as well. This algorithm has been widely applied in the real-time inspection of industrial welding seam. It has strong ability of adaption and self-teaching, higher inspection rate and real-time performance.
机译:本文采用自适应动量调节算法对各种照明参数进行标准化,以提高其对环境的自适应能力。通过PCA算法,原始数据空间被压缩为特征值模式空间,然后数据将聚集在次有效的特征值空间中。利用BP神经网络的改进对数据模式空间进行分类,可以实现系统的自学习功能,提高了正确率和实时性。该算法已广泛应用于工业焊缝的实时检测中。它具有很强的适应能力和自我教学能力,更高的检查率和实时性。

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