| Summary: | 碩士 === 元智大學 === 工業工程與管理學系 === 93 === ABSTRACT
In the statistical process control, control chart patterns recognition plays an important role. When the process existed may belong to the assignable causes, a control chart can present the specific non-random patterns. Correctly recognizes the non-random patterns may reduce examines scope of the regulation abnormal reason to be helpful to the improvement plan. The purpose of this research is to develop a system to recognize the trend, cycle, shift non-random patterns and the normal data effectively.
In this research, we proposed a neural network-based pattern recognizer for the analysis of control chart patterns. In addition, we have developed and compared with two kind of input vectors: raw data and features that extrascted from wavelet theory and multi-resolution analysis. The concepts of wavelet theory and multi-resolution analysis (MRA) have be used for data pre-processing. It seems more capable of detecting unnatural patterns as well as describing the key features of the specific pattern detected and the excuting time of artificial neural network.
Since the pattern recognizer of this research is appraised its benefit by the rate of correct classification. The results of simulation have show that the pattern recognizer using features based on wavelet theory and multi-resolution analysis can recognize the control chart patterns better than raw data and the structure of neural network is simply, too.
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