TY - GEN
T1 - Quantitative object motion prediction by an adaptive resonance theory (ART) neural network
AU - Zhu, Qiuming
PY - 1992
Y1 - 1992
N2 - An Adaptive Resonance Theory (ART) neural network is applied for the estimation and prediction of object motion states in real time. A bottom-up process of the network keeps track of the motion history of the object and a topdown process generates the prediction of the object motion. A retrospective enforcement process adjusts the network parameters to respond dynamically to the object motion. The process does not require any assumption of the object motion model and is applicable to a variety of situations where object motion exhibits irregular and abrupt variations.
AB - An Adaptive Resonance Theory (ART) neural network is applied for the estimation and prediction of object motion states in real time. A bottom-up process of the network keeps track of the motion history of the object and a topdown process generates the prediction of the object motion. A retrospective enforcement process adjusts the network parameters to respond dynamically to the object motion. The process does not require any assumption of the object motion model and is applicable to a variety of situations where object motion exhibits irregular and abrupt variations.
UR - http://www.scopus.com/inward/record.url?scp=0027103536&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0027103536&partnerID=8YFLogxK
U2 - 10.23919/acc.1992.4792015
DO - 10.23919/acc.1992.4792015
M3 - Conference contribution
AN - SCOPUS:0027103536
SN - 0780302109
SN - 9780780302105
T3 - Proceedings of the American Control Conference
SP - 41
EP - 45
BT - Proceedings of the American Control Conference
PB - Publ by American Automatic Control Council
T2 - Proceedings of the 1992 American Control Conference
Y2 - 24 June 1992 through 26 June 1992
ER -