TY - JOUR
T1 - Material characterization by ultrasonics using unsupervised competitive learning
AU - Chou, Chipai
AU - Ho, Bong
AU - Sheu, Jeng-Tzong
PY - 1995/1/1
Y1 - 1995/1/1
N2 - In this paper a competitive learning network based on a new "conscience" learning algorithm is presented. A number of algorithms for competitive learning networks are compared to the proposed algorithm. The proposed algorithm is tested with different data sets and is shown to be efficient in obtaining near-optimal results. Clustering results produced by the network are checked by an internal index for cluster validity. We conclude this paper with an application of the proposed network in acoustic imaging segmentation for material characterization.
AB - In this paper a competitive learning network based on a new "conscience" learning algorithm is presented. A number of algorithms for competitive learning networks are compared to the proposed algorithm. The proposed algorithm is tested with different data sets and is shown to be efficient in obtaining near-optimal results. Clustering results produced by the network are checked by an internal index for cluster validity. We conclude this paper with an application of the proposed network in acoustic imaging segmentation for material characterization.
KW - Artificial neural network
KW - Clustering
KW - Ultrasound material characterization
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=0029345956&partnerID=8YFLogxK
U2 - 10.1016/0167-8655(95)00018-C
DO - 10.1016/0167-8655(95)00018-C
M3 - Article
AN - SCOPUS:0029345956
SN - 0167-8655
VL - 16
SP - 769
EP - 777
JO - Pattern Recognition Letters
JF - Pattern Recognition Letters
IS - 7
ER -