基于新统计特征的神经网络控制图模式识别

M. K. Naeini, N. Bayati
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引用次数: 2

摘要

今天对于工艺偏差的识别和及时纠正的考察,有必要采用先进的技术,以尽量减少不良产品的生产成本。这样,控制图作为统计过程控制的重要工具之一,与人工神经网络等现代工具相结合,得到了广泛的应用。在一些研究中,人工神经网络被用来识别控制图中的模式。采用基于原始数据和特征的两种方法对神经网络进行训练和应用。本文提出了新的统计特征,并应用神经网络对其有效性进行了研究。仿真结果证明了所提出的统计特征对神经网络性能的积极影响。doi: 10.5829 / idosi.ije.2017.30.09c.10
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Pattern Recognition in Control Chart Using Neural Network based on a New Statistical Feature
Today for the expedition of the identification and timely correction of process deviations, it is necessary to use advanced techniques to minimize the costs of production of defective products. In this way control charts as one of the important tools for the statistical process control in combination with modern tools such as artificial neural networks have been used. The artificial neural networks were used to recognize the pattern in control charts in several research. Two procedures were used based on the raw data and feature for training and application of neural network. This paper presented new statistical features besides the investigation of their efficiency by application of a neural network. The simulation results demonstrated the positive effect of the presented statistical feature on neural network performance. doi: 10.5829/idosi.ije.2017.30.09c.10
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发文量
29
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