涡流检测中的组合神经网络模型

A Composite Neural Network Model for Eddy Current Testing

针对使用单一神经网络在缺陷识别中存在的输入矢量维数高 ,结构复杂及训练时间长等问题 ,本文提出了组合神经网络模型 ,这一模型采用逐级判别的方法 ,每级判断均采用独立的神经网络子模块 ,各模块采用随机学习算法分别进行训练 .裂缝识别的计算实例表明 ,这一组合模型使神经网络输入变量的维数从N2 降低到N ,网络结构大为简化 ,训练速度很快 ,同时具有较高的缺陷识别率 。

A composite neural network model with many sub network stages is presented.The flaw identification is accomplished stage by stage.Each stage consists of different sub networks and is trained by a stochastic learning algorithm independently.Numerical simulations of crack identification have shown that the composite neural network model decreases the input vector dimensions of neural network from N 2 to N ,simplifies the structure of network,saves the learning time,and has a better generalization ability.It can be used effectively in eddy current testing.