王晓燕, 刘〓辉, 苏〓纯, 白艳萍. 基于神经网络的激光超声探伤表面波的分类[J]. 信阳师范学院学报(自然科学版), 2016, 29(1): 120-123. DOI: 10.3969/j.issn.1003-0972.2016.01.029
引用本文: 王晓燕, 刘〓辉, 苏〓纯, 白艳萍. 基于神经网络的激光超声探伤表面波的分类[J]. 信阳师范学院学报(自然科学版), 2016, 29(1): 120-123. DOI: 10.3969/j.issn.1003-0972.2016.01.029
Wang Xiaoyan , Liu Hui , Su Chun , Bai Yanping . Classification of Surface Wave by Laser Ultrasonic Flaw Detection Based on Neural Network[J]. Journal of Xinyang Normal University (Natural Science Edition), 2016, 29(1): 120-123. DOI: 10.3969/j.issn.1003-0972.2016.01.029
Citation: Wang Xiaoyan , Liu Hui , Su Chun , Bai Yanping . Classification of Surface Wave by Laser Ultrasonic Flaw Detection Based on Neural Network[J]. Journal of Xinyang Normal University (Natural Science Edition), 2016, 29(1): 120-123. DOI: 10.3969/j.issn.1003-0972.2016.01.029

基于神经网络的激光超声探伤表面波的分类

Classification of Surface Wave by Laser Ultrasonic Flaw Detection Based on Neural Network

  • 摘要: 利用 3 种神经网络即自组织竞争神经网络、 学习向量量化神经网络和概率神经网络对激光超声探伤缺陷表面波进行分类 . 讨论了 3 种网络在不同输入情况下的分类效果 . 实验结果表明, 这 3 种神经网络都可以取得良好的分类效果 .

     

    Abstract: The surface wave was classified by using three kinds of neural networks, i.e. self-organizing competition neural network, learning vector quantization(LVQ) neural network and probabilistic neural network(PNN). Several experiments on different input situations for the three kinds of neural networks were discussed. Experimental results indicated that three kinds of neural networks had good performances in the classification.

     

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