Title :
Rough set and neural network based risk evaluation under coalmine with detect mobile robot
Author :
Jian, Lian ; Haitao, Pu ; Quanxin, Liu
Author_Institution :
Dept. of Electr. Eng. & Inf. Technol., Shandong Univ. of Sci. & Technol., Jinan, China
Abstract :
The objective of this paper is to present a novel method based on detect mobile robot for risk evaluation under coal mine, the approach is based on rough set and neural network theories, the data of the evaluation chart were reduced by using rough sets reduction function and then the reduced data were transferred to the BP neural network as training data. This method provides a new concept for the establishment of environmental safety assessment models. The result of the experiments shows that this method is valid for the assessment of the gas safety and the estimated result is very reliable.
Keywords :
backpropagation; coal; mobile robots; neural nets; risk analysis; rough set theory; BP neural network; coalmine; detect mobile robot; neural network; risk evaluation; rough sets reduction function; Classification algorithms; Fuzzy neural networks; Hazards; Neutrons; Robots; Training; Information fusion; mine robot; neural network; rough set; safety;
Conference_Titel :
IT in Medicine and Education (ITME), 2011 International Symposium on
Conference_Location :
Cuangzhou
Print_ISBN :
978-1-61284-701-6
DOI :
10.1109/ITiME.2011.6132105