Loading...

Artificial neural network in applying multi attribute control chart for AR processes

Akhavan Niaki, S. T ; Sharif University of Technology | 2010

782 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ICCAE.2010.5451471
  3. Publisher: 2010
  4. Abstract:
  5. Quality characteristics are subject of both manufacturing and service industries, which include not only the variables but the attributes as well. In Quality Control area substantial research has been done for Auto-correlated variables; however, no attempt was done for Auto-correlated attributes. Ignoring the autocorrelation structure in constructing control charts cause the in-control run length to decrease, and the false alarms to increase as such. In this article we develop a new methodology based upon the modified Elman neural network capabilities to overcome this problem. Moreover, instead of back propagation, simulated annealing is suggested as an alternative training technique that is able to search globally and in order to generate random AR vector we develop another artificial neural network based on ARTA algorithm. We present a simulation experiments and compare the performance of the proposed methodology with the other control methods of multi-attribute processes. The result of the simulation study is encouraging
  6. Keywords:
  7. ARTA ; Component ; Multi attribute control charts ; Neural network ; Artificial Neural Network ; Autocorrelation structures ; Autoregressive ; Control charts ; Control methods ; False alarms ; In-control ; Modified Elman neural networks ; Multi-attributes ; Quality characteristic ; Run length ; Service industries ; Simulation experiments ; Simulation studies ; Training techniques ; Computer simulation ; Customer satisfaction ; Flowcharting ; Neural networks ; Simulated annealing ; Total quality management ; Process control
  8. Source: 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010, 26 February 2010 through 28 February 2010, Singapore ; Volume 5 , 2010 , Pages 216-220 ; 9781424455850 (ISBN)
  9. URL: http://ieeexplore.ieee.org/document/5451471/?reload=true