发现基于概率神经网络的结构损伤定位方法能够正确识别单一位置损伤，且组合参数作为输入指标时的识别效果更好。
The result indicates that probabilistic neural networks can localize the single damage correctly, and the networks with the compounded index show better effectiveness.
基于径向基概率神经网络，提出一种扫描工程图纸图像分割后的图形符号识别方法。
A novel graphic symbol recognition approach of engineering drawings based on radial basis probabilistic neural networks (RBPNN) is proposed.
针对疲劳裂纹扩展寿命失效概率计算的复杂性，提出基于神经网络响应面的可靠性分析方法。
In response to the complexity of calculation for failure probability regarding fatigue crack growth life, a method for reliability analysis based on neural network response surface was presented.
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