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Title: Classification of behavior using unsupervised temporal neural networks

Abstract

Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem.

Authors:
 [1];  [2]
  1. Florida State Univ., Tallahassee, FL (United States). Dept. of Computer Science
  2. Los Alamos National Lab., NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE, Washington, DC (United States)
OSTI Identifier:
645492
Report Number(s):
LA-UR-97-4802; CONF-971068-
ON: DE98004362; TRN: 98:010137
DOE Contract Number:  
W-7405-ENG-36
Resource Type:
Conference
Resource Relation:
Conference: IEEE international conference on systems, man and cybernetics, Orlando, FL (United States), 12-15 Oct 1997; Other Information: PBD: Mar 1998
Country of Publication:
United States
Language:
English
Subject:
05 NUCLEAR FUELS; 99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; NEURAL NETWORKS; LEARNING; TIME DEPENDENCE; ON-LINE MEASUREMENT SYSTEMS; NUCLEAR FACILITIES; SECURITY

Citation Formats

Adair, K L, and Argo, P. Classification of behavior using unsupervised temporal neural networks. United States: N. p., 1998. Web.
Adair, K L, & Argo, P. Classification of behavior using unsupervised temporal neural networks. United States.
Adair, K L, and Argo, P. 1998. "Classification of behavior using unsupervised temporal neural networks". United States. https://www.osti.gov/servlets/purl/645492.
@article{osti_645492,
title = {Classification of behavior using unsupervised temporal neural networks},
author = {Adair, K L and Argo, P},
abstractNote = {Adding recurrent connections to unsupervised neural networks used for clustering creates a temporal neural network which clusters a sequence of inputs as they appear over time. The model presented combines the Jordan architecture with the unsupervised learning technique Adaptive Resonance Theory, Fuzzy ART. The combination yields a neural network capable of quickly clustering sequential pattern sequences as the sequences are generated. The applicability of the architecture is illustrated through a facility monitoring problem.},
doi = {},
url = {https://www.osti.gov/biblio/645492}, journal = {},
number = ,
volume = ,
place = {United States},
year = {1998},
month = {3}
}

Conference:
Other availability
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