在模型构造中，采用小波概率神经网络进行控制图的模式识别和控制图异常模式的参数估计。
In the modeling of the structure, the wavelet probabilistic neural network was used to recognize the control chart patterns and estimate the abnormal patterns parameters.
在对径向基概率神经网络进行理论分析基础上，采用减法聚类方法确定它的隐中心矢量。
On the basis of analyzing RBPNN in theory, subtractive clustering is used to determine its hidden centric vector.
该推理模型前级采用神经网络并行子网，用于目标的预分类，后级采用证据理论用于多周期的不确定性推理和概率的全局分配。
The forestage of the fusion model completes target presort and its post-stage is used to multi-period uncertainty inference and the whole set distribution of probability.
该推理模型前级采用神经网络并行子网，用于目标的预分类，后级采用证据理论用于多周期的不确定性推理和概率的全局分配。
The forestage of the fusion model completes target presort and its post-stage is used to multi-period uncertainty inference and the whole set distribution of probability.
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