针对时差定位法受很多因素影响的弊端，将神经网络技术应用到声发射源定位中。提取最能揭示声发射源的特征参数和运用主元分析技术来降低输入样本的数量；采用增加隐含层神经元个数探讨它们的误差变化来确定隐含层；运用附加动量法和优化选取初始阈值等措施进行网络设计。将设计好的网络运用到实例中,通过与实际缺陷位置的比较，结果表明，选择合理的网络结构和输入参数可准确定出结构损伤位置，且精度有较大的提高，计算更简单有效。

Due to defects of time-of-arrival localization that influenced by many factors, a neural network technique was used to predict localizations of the acoustic emission sources. In order to reduce numbers of input samples, the most important characteristic parameters of acoustic emission sources were put up and adopted techniques of principle component analysis (PCA), and the number of hidden units was determined by training the neural network using different numbers of hidden units. A BP network was designed by use of the additional momentum method and chosen initial threshold optimized. The network was used in an illustration, by comparing with results of actual damage localization, the results showed that a reasonable network structure and input parameters could determine accurately position of structural damage. In addition, the precision of localization was improved , computation became more efficiency and simpler.