基于小波神经网络的木质材料缺陷模式识别

利用小波和神经网络对木质材料中密度纤维板的不同缺陷进行智能模式识别，研究采用Daubechies小波包对振动信号进行3层分解，计算信号在各频段所占的能量率，并以此作为样本对拓扑结构不同的BP神经网络进行训练，然后利用训练好的网络对缺陷的种类进行分类识别。结果表明，性质相近的两种贫胶缺陷应作为一类缺陷模式进行识别，单隐层和双隐层的BP网络对没有缺陷、鼓泡缺陷和贫胶缺陷3种模式的识别都很理想，但双隐层BP网络的推广性能较好，网络输出的波动性小。对中密度纤维板没有缺陷、鼓泡缺陷和贫胶缺陷智能识别的最佳网络是双层BP网络，网络第1隐层节点和第2隐层节点分别为20和6，对中密度纤维板缺陷模式识别的准确率为90％。

Pattern Recognition of Wood Material Defects Using Wavelet and Artificial Neural Network

The wavelet analysis and neural network were employed to study the dif ferent defects of wood materials medium density fiberboard(MDF) by the way of pa ttern recognition. The Daubechies wavelet packet was used to decompose vibration signals with different defects of the materials, and the energy rate in each fr equency segment from the calculation, as the training sample, was used to train the different topological structure neural networks. Then, the trained networks were used to recognize the defects. The results show that two kinds of poor adhe sive defects with similar properties are recognized as one pattern; the BP netwo rks with one layer or two layers could recognize bubbling, poor adhesive and no defects extreme exactly; The generalization of the BP network with two layers is better than that with one layer; and the fluctuation of the BP network with two layers is smaller than that with one layer. The optimal BP network to recognize the three pattern of MDF is the two layer network with the hidden nodes of 20 and 6 respectively. And the accuracy of recognizing the MDF defects is 90%.