Main Title |
Using a neural network to estimate solvent consumption |
Author |
Capone, R. L. ;
Chappell, P. J. ;
|
Other Authors |
|
CORP Author |
Capone (Ronald L.) and Associates, Arlington, VA.;Environmental Protection Agency, Research Triangle Park, NC. Air and Energy Engineering Research Lab. |
Publisher |
U.S. Environmental Protection Agency, Office of Research and Development, Air and Energy Engineering Research Laboratory, |
Year Published |
1993 |
Report Number |
PB93-173110 ; EPA/600/A-93/063 ; EPA P.O. 2D1328NATA ; AEERL-P-1001 |
Stock Number |
PB93-173110 |
OCLC Number |
39877052 |
Additional Subjects |
Neural networks ;
Solvents ;
Study estimates ;
Pollutants ;
Environmental surveys ;
Forecasting ;
Graphic arts ;
Printing inks ;
Economic impact ;
Productivity ;
SIC 27
|
Holdings |
Library |
Call Number |
Additional Info |
Location |
Last Modified |
Checkout Status |
EKBD |
EPA-600/A-93/063 |
|
Research Triangle Park Library/RTP, NC |
09/25/1998 |
NTIS |
PB93-173110 |
Some EPA libraries have a fiche copy filed under the call number shown. |
|
07/26/2022 |
|
Collation |
4 p. : ill. ; 28 cm. |
Abstract |
The paper discusses a neural network, using the backpropagation paradigm, that is taught the relationship between employment in the graphic arts industry--(Standard Industrial Classification Code (SIC) 27)--and economic variables and solvent consumption by SIC 27. The project is a proof of concept whose objective is to a relationship using national-level data, which are known, and apply it to estimating solvent consumption on the county level, where data are thus far not available. The network accurately learns a relationship from national data. Although definitive testing is not yet possible due to data limitations, there are indications that the national relationship can be used to estimate county-level solvent consumption. Network inputs are SIC 27 employment, productivity for the current and 1 prior year, and an eight-element 'signature' of quarterly economic changes in output from non-durable industries. One hidden layer of two processing elements connects the 11-element input layer to a 1-element output layer. NeuralWare Professional II Plus Version 4.0 was used as the platform. Training requires 30,000 iterations and results in a Pearson's r value of 0.99. The best result achieved by ordinary least squares regression was 0.93. |
Notes |
Cover verso title. EPA Project Officer: P. Jeff Chappell. "EPA/600/A-93/063." "PB93-173110." |
Place Published |
Research Triangle Park, N.C. : |
Supplementary Notes |
Presented at IEEE Conference on Artificial Intelligence for Applications (9th), Orlando, FL., March 1-5, 1993. Sponsored by Environmental Protection Agency, Research Triangle Park, NC. Air and Energy Engineering Research Lab. |
Corporate Au Added Ent |
Air and Energy Engineering Research Laboratory. ; Ronald L. Capone and Associates. |
PUB Date Free Form |
{1993} |
BIB Level |
m |
Cataloging Source |
OCLC/T |
OCLC Time Stamp |
19980916090217 |
Language |
eng |
Origin |
OCLC |
Type |
MERGE |
OCLC Rec Leader |
01333nam 2200313Ka 45020 |