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Parameters optimization for exponentially weighted moving average control chart using generalized regression neural network
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作者 梁宗保 《Journal of Chongqing University》 CAS 2006年第3期131-136,共6页
As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was... As a useful alternative of Shewhart control chart, exponentially weighted moving average (EWMA) control chat has been applied widely to quality control, process monitoring, forecast, etc. In this paper, a method was introduced for optimal design of EWMA and multivariate EWMA (MEWMA) control charts, in which the optimal parameter pair ( λ, k) or ( λ, h ) was searched by using the generalized regression neural network (GRNN). The results indicate that the optimal parameter pair can be obtained effectively by the proposed strategy for a given in-control average running length (ARLo) and shift to detect under any conditions, removing the drawback of incompleteness existing in the tables that had been reported. 展开更多
关键词 parameter optimization exponentially weighted moving average control chart generalized regression neural network
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