改进神经网络煤矿安全评价模型仿真研究

Improved Neural Network Model for Simulation of Coal Mine Safety Assessment

研究煤矿安全评价准确性问题。煤矿生产安全问题一直是国内外研究的热点,针对传统的安全评价算法难以评价出煤矿安全生产中出现的情况,评价预测准确率低等问题,提出了基于BP神经网络算法煤矿安全评价模型。采用BP神经网络的特点是可以逼近任意的非线性函数,但是BP神经网络并非完美的神经网络,采用遗传算法优化BP神经网络可以克服其缺点,将改进的算法应用于煤矿系统安全评价之中,仿真结果表明,基于改进的BP神经网络煤矿安全评价模型方法有效性和实用性,能够正确评价安全生产状态。

The problem of coal niine safety assessment model was studied. Coal mine production safety issue has been a research focus at home and abroad, for the traditional algorithm is difficult to evaluate the safety assessment of coal mine production safety situation. Evaluation algorithm of prediction was proposed based on BP neural network model for coal mine safety assessment. Using BP neural network can approach any nonlinear function, but the BP neural network is not a perfect neural network. Using the genetic algorithm to optimize BP neural network can overcome its shortcomings, and the improved system safety assessment method was used in coal mine. Simulation results show that the improved BP neural network model for coal mine safety evaluation is effective and practical.