Rs. Ransing et Rw. Lewis, A SEMANTICALLY CONSTRAINED NEURAL-NETWORK FOR MANUFACTURING DIAGNOSIS, International Journal of Production Research, 35(9), 1997, pp. 2639-2660
Citations number
18
Categorie Soggetti
Engineering,"Operatione Research & Management Science
In an earlier work (Ransing et al. 1995), we represented the causal re
lationship in a defect-metacause-rootcause form. This representation w
as perceived to be of considerable importance to the research communit
y as well as industry, as it is applicable to any form of manufacturin
g process. Based on this representation we proposed 'A Semantically Co
nstrained Bayesian Network' for the diagnostic problems (Lewis and Ran
sing 1997). In this paper, we develop another popular Artificial Intel
ligence tool, 'Feedforward Neural Network', for such diagnostic proble
ms. The network is constrained to defect-metacause-rootcause topology
and it has been shown that metacause concepts can be successfully asso
ciated with the hidden nodes. The errors are calculated at both the ou
tput layer and the hidden layer. Although the learning process is base
d on the back-propagation algorithm with a momentum term, the weight c
hanges would occur at a link connecting a node only if at least one of
the nodes connected to it in the preceding layer has non-zero activat
ion. The theoretical analysis of such constrained learning is given an
d it is shown that the network behaviour is acceptable for the diagnos
tic problems considered.