Cultural advice

The Australian National University acknowledges, celebrates and pays our respects to the Ngunnawal and Ngambri people of the Canberra region and to all First Nations Australians on whose traditional lands we meet and work, and whose cultures are among the oldest continuing cultures in human history.

Aboriginal and Torres Strait Islander peoples are advised that ANU Library collections may include images, names, voices, and other representations of deceased persons.

Material in the collection may contain terms, language or views that reflect the period in which the item was created and may be considered inappropriate today.

Deterministic Learning and Rapid Dynamical Pattern Recognition

Date

Authors

Wang, Cong

Hill, David

Publisher

Institute of Electrical and Electronics Engineers (IEEE Inc)

Abstract

Recognition of temporal/dynamical patterns is among the most difficult pattern recognition tasks. In this paper, based on a recent result on deterministic learning theory, a deterministic framework is proposed for rapid recognition of dynamical patterns.

Keywords

Keywords: Computer simulation; Convergence of numerical methods; Learning systems; Pattern matching; Synchronization; Deterministic learning; Dynamical pattern recognition; Similarity; Pattern recognition; algorithm; article; artificial intelligence; artificial neu Deterministic learning; Dynamical pattern recognition; Representation; Similarity; Synchronization

URI

http://hdl.handle.net/1885/29124

Collections

ANU Research Publications

Source

IEEE Transactions on Neural Networks

Type

Journal article

DOI

10.1109/TNN.2006.889496

Restricted until

2037-12-31

Downloads

File

Description

01_Wang_Deterministic_Learning_and_2007.pdf (1.4 MB)

abcd