TY - JOUR
T1 - Similarity analysis for robot motions using an FNN learning mechanism
AU - Young, Kuu-Young
AU - Wang, Jyh Kao
PY - 1997/12/12
Y1 - 1997/12/12
N2 - Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning-space complexity and motion variety require them to consume excessive amount of memory. We propose using a Fuzzy Neural Network (FNN) to learn and analyze robot motions so they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources.
AB - Learning controllers are usually subordinate to conventional controllers in governing multiple-joint robot motion, in spite of their ability to generalize, because learning-space complexity and motion variety require them to consume excessive amount of memory. We propose using a Fuzzy Neural Network (FNN) to learn and analyze robot motions so they can be classified according to similarity. After classification, the learning controller can then be designed to govern robot motions according to their similarities without consuming excessive memory resources.
UR - http://www.scopus.com/inward/record.url?scp=0031342128&partnerID=8YFLogxK
U2 - 10.1109/CDC.1997.657691
DO - 10.1109/CDC.1997.657691
M3 - Conference article
AN - SCOPUS:0031342128
SN - 0191-2216
VL - 3
SP - 2523
EP - 2528
JO - Proceedings of the IEEE Conference on Decision and Control
JF - Proceedings of the IEEE Conference on Decision and Control
M1 - 657691
T2 - Proceedings of the 1997 36th IEEE Conference on Decision and Control. Part 1 (of 5)
Y2 - 10 December 1997 through 12 December 1997
ER -