关键词: 质量控制, 模式识别, 遗传算法, 概率神经网络

Abstract: The core of quality control lies in the full utilization and analysis of quality-related data.Aiming at the problems of the loss of abnormal instances and the lag of quality anomaly discovery in the quality database,a method of recognizing quality anomaly from the quality control chart data by combining genetic algorithm with Probabilistic Neural Network (PNN) was proposed,which made up deficiencies of widely using SPC control chart in practical applications.The abnormal pattern of control chart was derived by analyzing the insufficiency of judgment criterion in the abnormality judgment of control graph.To reduce the training time of the model,the Principal Component Analysis (PCA) method was adopted for reducing the dimension and extracting feature for the original data of control map.PNN network had the advantages of simple structure and good recognition effect,which could realize the recognition of single mode and mixed mode of control chart.To eliminate the lack of experience value,the improved SGA single target optimization genetic algorithm was used to optimize the key parameters of PNN network.The proposed method was validated with simulation experiment and proved to be effective by comparing to traditional BP neural network,single PNN network,PCA-PNN model without parameters optimization and SVM model optimized by PSO particle swarm optimization algorithm.

Key words: quality control, pattern recognition, genetic algorithms, probabilistic neural network