A adaptive PNN is presented to improve the basic PNN and to compare with the basic PNN.
针对基本pnn的不足之处，提出了自适应pnn，并将其损伤识别精度与基本的PNN进行比较。
A novel PNN model with training algorithms is proposed for class conditional density estimation.
提出了一种新的类条件密度函数估计的PNN模型及其算法。
Finally, PNN method is used to identify the primary fault sources from the features of correlative faults.
最后，利用概率神经网络技术进一步从关联故障特征中辨识出初始故障源。
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