S. Rajasekaran, TRAINING-FREE COUNTER PROPAGATION NEURAL-NETWORK FOR PATTERN-RECOGNITION OF FABRIC DEFECTS, Textile research journal, 67(6), 1997, pp. 401-405
We present an application of a Training-free counter propagation netwo
rk (TFCPN) to detect fabric defects. The TFCPN, which is a modificatio
n of Hecht-Nielsen's counter propagation network (CPN), learns through
a simple recording algorithm devoid of any training, while retaining
the topology of the CPN model. The mathematical justification for the
modification is also presented. Four kinds of fabric defects-neps, bro
ken ends, broken picks, and oil stains-most likely to be found during
weaving are considered for recognition by the network. Results show th
at fabric defects such as these inspected by means of image recognitio
n in accordance with the TFCPN agree approximately with initial expect
ations. The CPN reported in this paper is training-free, and it can le
arn complicated textile design problems.