基于随机隐含层权值神经网络的瓦斯浓度预测

Gas concentration prediction based on neural network with random hidden weight

煤矿的安全生产一直是人们重点研究的课题之一。在众多的煤矿开采安全事故中,瓦斯引起的事故占到了大多数。对井下生产线的瓦斯浓度进行实时准确的预测,提前预知生产环境是否处于安全状态,对煤矿的安全生产来说意义重大。针对这一问题,提出了一种基于NSGA-II训练的随机隐含层神经网络(BNSGA-II NN)来进行瓦斯浓度预测的方法。一方面,NSGA-II需要设定的参数少,使用较为简单;另一方面,NSGA-II中的交叉变异机制避免了陷入局部最优解。为了证明NSGA-II训练的随机隐含权值神经网络的预测质量,通过实验与PSOGSA训练的随机隐含层神经网络(PSOGSA NN)进行了对比。实验结果表明,BNSGA-II NN的预测质量明显高于PSOGSA NN的预测质量。

The safe production of coal mines has always been one of human's key research subjects. In numerous safety accidents in coal mining, gas accidents account for most of them. Real-time and accurate prediction of gas concentration in underground production lines and anticipating whether the production environment is in a safe state is critical for the safety of coal mines. Aiming at this problem, we propose a gas concentration prediction method based on the random hidden layer neural network trained by NSGA-II (BNSGA-II NN). On the one hand, fewer parameters need to be set in the NSGA-II, and they are convenient to use. On the other hand, the cross variation mechanism in the NSGA-II avoids the problem of falling into local optimal solution in the traditional methods. To demonstrate the prediction quality of the trained neural network with random hidden weight using the NSGA-II, we compare the BNSGA-II NN with PSOGSA NN through experiments. Experimental results show that the prediction effect of the BNSGA-II NN is significantly better than that of the PSOGSA NN.