شماره ركورد كنفرانس :
3222
عنوان مقاله :
Control Chart Pattern Recognition Using Adaptive Back-propagation Artificial Neural Networks and Efficient Features
پديدآورندگان :
Addeh Jalil Faculty of Electrical and Computer Engineering - Babol University of Technology , Babaee Hossein Faculty of Electrical and Computer Engineering - Babol University of Technology , Ebrahimzadeh Ata Faculty of Electrical and Computer Engineering - Babol University of Technology
كليدواژه :
Control chart patterns , Neural networks , adaptive back-propagation , Statistical feature , shape features
عنوان كنفرانس :
دومين كنفرانس بين المللي كنترل، ابزار دقيق و اتوماسيون
چكيده لاتين :
Control chart patterns are important statistical process control tools for determining whether a process is run
in its intended mode or in the presence of unnatural patterns.Accurate recognition of control chart patterns is essential for
efficient system monitoring to maintain high-quality products.This paper introduces a novel hybrid intelligent system that
composed of two major decision layers. The patterns divided into three binary groups using Statistical feature and Neural
networks in the first layer. In the second layer, in each of groups, recognition is done using shape features and Neural
networks. One of these features is novel in this area. In learning of neural networks, indifference of training algorithm
due to parameter change has an important role in succession of an algorithm. Therefore adaptive back-propagation
algorithm is applied for training of neural networks. Simulation results show that the proposed system has high
recognition accuracy.