The risk early-warning of gas hazard in coal mine based on Rough Set-neural network

• 来源期刊: 煤炭学报（英文版）
• 年，卷（期）: 2007,（4）
• 页码范围: 400-404
• 页数: 5页
• 语种: 英文

摘要

This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexity of neural network system and the computing time was reduced, as well. Because of fault-tolerant ability, parallel processing ability, anti-jamming ability and processing non-linear problem ability of neural network system, the methods of Rough Set and neural network were combined. The examples research indicate: applying Rough Set and BP neural network to the gas hazard risk early-warning coal mines in coal mine, the BPNN structure is greatly simplified, the network computation quantity is reduced and the convergence rate is speed up.