A Neural Network-based Two-Sided Control Procedure for Monitoring and Characterizing the Mean Shifts of Autocorrelated Process
| Autor: | Hui-Ping Yang, 楊慧萍 |
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| Rok vydání: | 2005 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 93 In the standard applications of statistical process control, a state of statistical control is identified with a process generating independent and identically distributed random variables. In fact, the assumption of uncorrelated or independent observations is not even approximately satisfied in some manufacturing processes. A scheme of neural network is based on a function of multiple correlation coefficients instead of single correlation coefficient on traditional control charts. They are used to eliminate disadvantages of control charts and detect the process mean shift quickly and correctly. The main idea of neural network-based approach is to monitor the process mean shifts and detect process mean shifts and estimate the magnitude of shifts. We use the back-propagation neural network to solve these problems. A process control method will be more effective if the adjusted magnitude can be estimated correctly in this research of process mean changed. The performance of neural networks are evaluated by the average run lengths (ARL) and mean absolute percent errors (MAPE) using simulation. From simulation data of this model and developed results, the neural network method strongly outperforms EWMA charts and can be wildly adapted to many production processes. In this study, a neural network-based control method offers three main advantages. First, this method offers good detecting ability for most and most of the processes under any level of autocorrelation coefficient. Second, the proposed control procedures effectively eliminate errors and disadvantages of parameter estimating on residual control chart. Last, neural network control method is capable of detecting process of mean upwards and downward shift as well as traditional control charts. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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