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

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.