It is very difficult to generalize the relationship between MFL signal and the defect geometric parameters of the pipeline because the relationship is nonlinear. Many applications of wavelet neural network on this field show that the defect geometric parameters can be obtained with this method to some extent. However, the initial centers have great influence on performance of the network. Hierarchical clustering algorithm is proposed in this paper and applied to classification of defect samples, centers selection and calculation of basis function width. With this algorithm, clusters similarity is computed to create tree structure and the perfect clustering is obtained. The sample set created from finite element defect simulation are used to train and validate the efficiency and reliability of the network based on hierarchical clustering algorithm. The experiment shows that the training speed and the prediction precision of the network can be improved simulataneously.

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