A Comparison of Nonlinear Regression and Neural Network Models for Ground-Level Ozone Forecasting-Reference-Cited by-同舟云学术

A Comparison of Nonlinear Regression and Neural Network Models for Ground-Level Ozone Forecasting

Published:2000-11 Issue:11 Volume:50 Page:1999-2009
ISSN:1096-2247
Container-title:Journal of the Air & Waste Management Association
language:en
Short-container-title:Journal of the Air & Waste Management Association

Author:

Cobourn W. Geoffrey¹,Dolcine Leslie²,French Mark²,Hubbard Milton C.³

Affiliation:

1. a Department of Mechanical Engineering , University of Louisville , Louisville , Kentucky , USA

2. b Department of Civil and Environmental Engineering, Speed Scientific School , University of Louisville , Louisville , Kentucky , USA

3. c Automated Analysis Corporation , Peoria , Illinois , USA

Publisher

Informa UK Limited

Subject

Management, Monitoring, Policy and Law,Waste Management and Disposal

Link

https://www.tandfonline.com/doi/pdf/10.1080/10473289.2000.10464228

Reference8 articles.

1. Comrie, A.C. Comparing Neural Networks and Regression Models for Ozone Forecasting; /. Air & Waste Manage. Assoc. 1997, 47, 653663.

2. Jones, W.P.; Hoskins, J. Back-Propagation, a Generalized Delta Learning Rule; BYTE 1987, 12, 155–162.

3. Artificial Neural Networks: Theoretical Concepts; Vemuri, V., Ed.; Computer Society Press Technology Series; Computer Society of Institute of Electrical and Electronics Engineers: Washington, DC, 1988.

4. Simpson, P.K. Artificial Neural Systems, Foundations, Paradigms, Application, and Implementation; Pergamon: Elmsford, NY, 1990.

5. Schalkoff, R. Pattern Recognition: Statistical, Structural and Neural Approaches; Wiley: New York, 1992.

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