A Review of Artificial Neural Network Applications in Control Chart Pattern Recognition
نویسنده
چکیده
On-line automated process analysis is an important area of research since it allows the interfacing of process control with computer integrated manufacturing (CIM) techniques. The inflexibility and high computational costs of traditional SPC pattern recognition methodologies have led researchers to investigate artificial neural network applications to control chart pattern recognition. This paper addresses the current state of control chart pattern recognition using artificial neural networks and presents areas for future research.
منابع مشابه
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 netw...
متن کاملA New Statistical Approach for Recognizing and Classifying Patterns of Control Charts (RESEARCH NOTE)
Control chart pattern (CCP) recognition techniques are widely used to identify the potential process problems in modern industries. Recently, artificial neural network (ANN) –based techniques are very popular to recognize CCPs. However, finding the suitable architecture of an ANN-based CCP recognizer and its training process are time consuming and tedious. In addition, because of the black box ...
متن کاملLIQUEFACTION POTENTIAL ASSESSMENT USING MULTILAYER ARTIFICIAL NEURAL NETWORK
In this study, a low-cost, rapid and qualitative evaluation procedure is presented using dynamic pattern recognition analysis to assess liquefaction potential which is useful in the planning, zoning, general hazard assessment, and delineation of areas, Dynamic pattern recognition using neural networks is generally considered to be an effective tool for assessing of hazard potential on the b...
متن کاملIssues in Development of Artificial Neural Network-Based Control Chart Pattern Recognition Schemes
Control chart pattern recognition has become an active area of research since late 1980s. Much progress has been made, in which there are trends to heighten the performance of artificial neural network (ANN)-based control chart pattern recognition schemes through feature-based and wavelet-denoise input representation techniques, and through modular and integrated recognizer designs. There is al...
متن کاملControl chart pattern recognition using semi-supervised learning
This paper presents a semi-supervised learning algorithm for a control chart pattern recognition system. A learning neural network is trained with labeled control chart patterns based on unsupervised learning. We then use the classification method based on a statistical correlation coefficient approach to test patterns. We find that the proposed semi-supervised learning algorithm is effective a...
متن کامل