Study on Photometric Performance Attenuation Law of Retroreflective Materials for Road Traffic

Article Preview

Abstract:

The testing and evaluation of the retroreflective materials are the basic work of the road traffic engineering quality. In this paper, artificial neural networks are used to make the study on the photometric performance attenuation law of retroreflection materials. Through the establishment of photometric prediction models to forecast reflection coefficient of the different types of retroreflective sheeting, compared with the testing values, the results showed that photometric performance attenuation law can predict the coefficient of retroreflection of retroreflective sheeting. This conclusion is important for the application of retroreflective materials.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

3568-3573

Citation:

Online since:

March 2012

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2012 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Lapendes A, Farber. Nonlinear signal processing using neural networks: predication and system modeling[R], Technical Report LA-UR-87-2662. Los Angeles National Laboratory, Los Angeles. NM,1987.

Google Scholar

[2] Werbos PJ. Generalization of back propagation with application to a recurrent gas market model[J]. Neural Networks, 1988,(1):339~356.

DOI: 10.1016/0893-6080(88)90007-x

Google Scholar

[3] Varfis A, Versino C. Univariate economic time series forecasting by connectionist methods[C]. IEEE ICNN-90,1990,342~345.

Google Scholar

[4] Weigend A B et al. Predicting the future, a connectionist approach[J]. Intl.J.Neur.Sys, 1990.

Google Scholar

[5] White H. Connectionist nonparametric regression: multilayer feedforward networks can learn arbitrary mappings[J].Neural Networks,1990.

DOI: 10.1016/0893-6080(90)90004-5

Google Scholar

[6] Marquez L et al. Neural network model as an alternative to regression[C].in Proceeding of the 24th Hawaii International Conference on System Science,1991.

Google Scholar