Aiming at the problems of the lack of abnormal instances and the lag of quality anomaly discovery in quality database, this paper proposed the method of recognizing quality anomaly from the quality control chart data by probabilistic neural network (PNN) optimized by improved genetic algorithm, which made up deficiencies of SPC control charts in practical application. Principal component analysis (PCA) reduced the dimension and extracted the feature of the original data of a control chart, which reduced the training time of PNN. PNN recognized successfully both single pattern and mixed pattern of control charts because of its simple network structure and excellent recognition effect. In order to eliminate the defect of experience value, the key parameter of PNN was optimized by the improved (SGA) single-target optimization genetic algorithm, which made PNN achieve a higher rate of recognition accuracy than PNN optimized by standard genetic algorithm. Finally, the above method was validated by a simulation experiment and proved to be the most effective method compared with traditional BP neural network, single PNN, PCA-PNN without parameters optimized, and SVM optimized by particle swarm optimization algorithm.

中文翻译：

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基于优化概率神经网络的质量异常识别方法研究

针对质量数据库中缺乏异常情况和质量异常发现滞后的问题，提出了一种通过改进遗传算法优化的概率神经网络（PNN）从质量控制图数据中识别质量异常的方法。弥补了SPC控制图在实际应用中的不足。主成分分析（PCA）减少了维数并提取了控制图原始数据的特征，从而减少了PNN的训练时间。PNN具有简单的网络结构和出色的识别效果，因此可以成功识别控制图的单一模式和混合模式。为了消除经验值的缺陷，通过改进的（SGA）单目标优化遗传算法对PNN的关键参数进行了优化，与标准遗传算法优化的PNN相比，PNN的识别准确率更高。最后，通过仿真实验验证了上述方法，与传统的BP神经网络，单参数PNN，未经参数优化的PCA-PNN和采用粒子群优化算法优化的支持向量机相比，是最有效的方法。