International Journal of Man-Machine Studies
| Hi-index | 0.00 |
System reliability early warning aims to measure the rate of tolerance deviation from the reliability index borderline of the system running states, to confirm the early warning grade, and to assist decision-making warning. Analyzed and compared the methods of system early warning from home and abroad, and combined Rough Set (RS) theory and Artificial Neuron Network (ANN) technique, a new method based on Rough Set and Artificial Neuron Network (RS-ANN) to solve ventilation system reliability early warning is put forward. Firstly, an index system which adapted to ANN analysis for mine ventilation system reliability early warning is established. Secondly, a prepositive system with RS method to optimize the index system of ANN is put forward. Thirdly, the simulation model for ventilation system reliability early warning which based on RS-ANN is set up. Finally, the effectiveness of this method is proved by an example. Computer simulation shows that the simulation result by RSANN methods is consistent with the result of ANN analysis, but the training efficiency increased 667 times.