Cart (Loading....) | Create Account
Close category search window
 

Neural Network Modeling of Vehicle Gross Emitter Prediction Based on Remote Sensing Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
3 Author(s)
Huafang Guo ; Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou ; Jun Zeng ; Yueming Hu

Vehicle emissions are a significant source of air pollution in cities. A neural network model for vehicle gross emitter prediction was established based on remote sensing data. The states of vehicle emission remote sensing system in China were described first, followed by a brief introduction to idle testing and remote sensing testing. After data collection, the choices in the algorithm and architecture, as well as original data were then analyzed and compared. The back-propagation (BP) neural network model with 7-20-1 architecture was also selected as the optimal approach with satisfied prediction. Compared with traditional model, the proposed approach has better accuracy and generality. The 81.63% correct results show the potentiality and validity of remote sensing for gross emitter prediction by using the neural network

Published in:

Networking, Sensing and Control, 2006. ICNSC '06. Proceedings of the 2006 IEEE International Conference on

Date of Conference:

0-0 0

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.