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在分析传统控制图在实际应用中存在的缺陷的基础上 ,提出了一种利用模糊集和人工神经网络技术进行工序失控原因诊断的解决办法。与其它模式识别方法对比 ,该方法具有较强的抗干扰能力和较高的灵敏度。通过该方法的应用 ,可以及时发现工序存在的问题并加以改进 ,为统计过程控制向统计过程控制及诊断的发展提供了一条新的可行的途径
Based on the analysis of the defects in the practical application of the traditional control charts, a solution to the cause of the out of control diagnosis is proposed by using fuzzy sets and artificial neural networks. Compared with other pattern recognition methods, this method has strong anti-interference ability and high sensitivity. Through the application of this method, the existing problems in the process can be found and improved in time, providing a new feasible approach for the development of statistical process control and diagnosis for statistical process control