ANALYSIS OF PERFORMANCE OF BACKPROPAGATION ANN WITH DIFFERENT TRAINING PARAMETERS. IN: NEURAL NETWORKS IN TRANSPORT APPLICATIONS
Three inputs to a typical backpropagation based artificial neural network (ANN) modelling procedure are the number of hidden units, the learning rate (LR), and the momentum constant (MC). These three inputs have a profound effect on the ANN training as well as the resulting behavior of a trained network. This paper follows research done for the purpose of modeling trip generation using regression analysis and ANNs. The paper first presents a brief introduction to the problem of trip generation, and then explains the database used for modeling. The results of backpropagation modeling are also presented, followed by conclusions and recommendations.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/isbn/184014808X
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Corporate Authors:
Ashgate Publishing Company
110 Cherry Street, Suite 3-1
Burlington, VT United States 05401-3818 -
Authors:
- Faghri, A
- Sandeep, A
- Publication Date: 1998
Language
- English
Media Info
- Features: Figures; References; Tables;
- Pagination: p. 57-84
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Serial:
- Atmospheric Environment
- Publisher: Elsevier
- ISSN: 1352-2310
- Serial URL: http://www.sciencedirect.com/science/journal/13522310
Subject/Index Terms
- TRT Terms: Artificial intelligence; Backpropagation; Model atmosphere; Neural networks; Regression analysis; Training; Trip generation
- Subject Areas: Data and Information Technology; Education and Training; Highways; Planning and Forecasting;
Filing Info
- Accession Number: 00796279
- Record Type: Publication
- ISBN: 184014808X
- Files: TRIS
- Created Date: Jul 25 2000 12:00AM