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Title: Exploring neural network technology

Abstract

EPRI is funding several projects to explore neural network technology, a form of artificial intelligence that some believe may mimic the way the human brain processes information. This research seeks to provide a better understanding of fundamental neural network characteristics and to identify promising utility industry applications. Results to date indicate that the unique attributes of neural networks could lead to improved monitoring, diagnostic, and control capabilities for a variety of complex utility operations. 2 figs.

Authors:
;
Publication Date:
OSTI Identifier:
6974181
Resource Type:
Journal Article
Journal Name:
EPRI Journal (Electric Power Research Institute); (United States)
Additional Journal Information:
Journal Volume: 17:8; Journal ID: ISSN 0362-3416
Country of Publication:
United States
Language:
English
Subject:
20 FOSSIL-FUELED POWER PLANTS; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; ELECTRIC UTILITIES; COMPUTERIZED CONTROL SYSTEMS; NEURAL NETWORKS; TECHNOLOGY ASSESSMENT; ARTIFICIAL INTELLIGENCE; COMPUTER ARCHITECTURE; OPERATION; PATTERN RECOGNITION; CONTROL SYSTEMS; ON-LINE CONTROL SYSTEMS; ON-LINE SYSTEMS; PUBLIC UTILITIES; 200104* - Fossil-Fueled Power Plants- Components; 990200 - Mathematics & Computers

Citation Formats

Naser, J, and Maulbetsch, J. Exploring neural network technology. United States: N. p., 1992. Web.
Naser, J, & Maulbetsch, J. Exploring neural network technology. United States.
Naser, J, and Maulbetsch, J. 1992. "Exploring neural network technology". United States.
@article{osti_6974181,
title = {Exploring neural network technology},
author = {Naser, J and Maulbetsch, J},
abstractNote = {EPRI is funding several projects to explore neural network technology, a form of artificial intelligence that some believe may mimic the way the human brain processes information. This research seeks to provide a better understanding of fundamental neural network characteristics and to identify promising utility industry applications. Results to date indicate that the unique attributes of neural networks could lead to improved monitoring, diagnostic, and control capabilities for a variety of complex utility operations. 2 figs.},
doi = {},
url = {https://www.osti.gov/biblio/6974181}, journal = {EPRI Journal (Electric Power Research Institute); (United States)},
issn = {0362-3416},
number = ,
volume = 17:8,
place = {United States},
year = {Tue Dec 01 00:00:00 EST 1992},
month = {Tue Dec 01 00:00:00 EST 1992}
}