The road tunnel fire detection of multi-parameters based on BP neural network

Author

Xue Cai Jiao

Author_Institution

Basic Courses Teaching Dept., Armed Police Acad., Langfang, China

Volume

3

fYear

2010

fDate

6-7 March 2010

Firstpage

246

Lastpage

249

Abstract

The temperature, the smoke density and the density of CO are selected as the main parameters of road tunnel fire detection, analyzing the environment and the characteristics of road tunnel fire. Based on BP neural network, the mathematical model is set up. Studying the algorithm of BP neural network, the model can identify road tunnel fire. The simulation result of road tunnel fire shows that the correct recognition probability is high. The false-alarm probability can be decreased. The suitability of this system to environmental variation can be obviously improved. The study and application of this method has a great theoretical and practical value for early prediction of road tunnel fires.

Keywords

backpropagation; fires; neural nets; roads; structural engineering computing; tunnels; BP neural network; false-alarm probability; mathematical model; recognition probability; road tunnel fire detection; smoke density; Fires; Gas detectors; Multi-layer neural network; Neural networks; Road accidents; Robotics and automation; Smoke detectors; Space heating; Temperature sensors; Ventilation;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on

Conference_Location

Wuhan

ISSN

1948-3414

Print_ISBN

978-1-4244-5192-0

Electronic_ISBN

1948-3414

Type

conf

DOI

10.1109/CAR.2010.5456677

Filename

5456677