Quantitative object motion prediction by an adaptive resonance theory (ART) neural network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
PublisherPubl by American Automatic Control Council
Pages41-45
Number of pages5
ISBN (Print)0780302109
Publication statusPublished - Dec 1 1992
EventProceedings of the 1992 American Control Conference - Chicago, IL, USA
Duration: Jun 24 1992Jun 26 1992

Publication series

NameProceedings of the American Control Conference
Volume1
ISSN (Print)0743-1619

Other

OtherProceedings of the 1992 American Control Conference
CityChicago, IL, USA
Period6/24/926/26/92

    Fingerprint

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Zhu, Q. (1992). Quantitative object motion prediction by an adaptive resonance theory (ART) neural network. In Proceedings of the American Control Conference (pp. 41-45). (Proceedings of the American Control Conference; Vol. 1). Publ by American Automatic Control Council.