在二维漏磁缺陷重构中,建立基于径向基小波神经网络(RWBF)的正演和反演模型,提出了一个反馈形式的双小波神经网络迭代模型,通过迭代使目标函数最小化,实现对缺陷轮廓的快速逼近.用仿真和实验获取的训练样本分别对正演和反演模型的RWBF进行训练.为了提高径向基神经网络的适应性和精度,提出了一种新的训练算法.首先确定最优分解层数,然后利用梯度下降法修正网络的权值.对不同分辨率和不同信噪比下的漏磁信号进行了重构,并与其他方法进行了比较.结果表明,双小波神经网络迭代模型能够实现漏磁缺陷的精确逼近,具有良好的鲁棒性,是有效的二维轮廓重构方法.%To reconstruct 2-D defect profile from magnetic flux leakage (MFL) signals, a dual wavelet neural network iteration model, including a forward model and an inverse model, baaed on radial wavelet basis function neural network was proposed. It iteratively adjusts the weights of the inverse network to minimize the error between the measured and predicted MFL signals. The network can be trained respectively by the same training samples from measurement and FEM calculation. To improve the network' s a-daptability and accuracy, a novel training algorithm was proposed. Firstly, confirm the optimal number of layers, and then update the weights based on the conjugate gradient algorithm. The reconstruction results in different resolutions and SNRs indicate that the method is rapid, accurate and robust, and it is effective and feasible for reconstruction of 2-D defects comparing with other approaches.
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