基于量子遗传算法的煤矿安全评价模型

煤矿安全评价涉及诸多不确定信息,用传统评价方法难以确保最后评价结果的准确性和可靠性。为此利用RBF神经网络设计出煤矿综合安全评价模型,根据我国煤矿的实际情况,通过事故树(FTA)和事件树(ETA)分析,归纳煤矿事故发生的危险因素和影响矿井生产的不安全因素。同时为了克服神经网络易陷入局部最小,研究采用量子遗传算法对神经网络模型的权值(阈值)进行优化。将该方法应用在阜新矿业集团公司某矿,结果表明,该模型可以准确地评价煤矿安全生产,为煤矿安全评价提供一条新的途径,对煤矿安全生产起到了重要的指导意义。

Coal Mine Safety Evaluation Model Based on Quantum Genetic Algorithm

The coal mine safety evaluation involves many uncertain information.It is difficult to ensure the accuracy and reliability of finall consequence using traditional evaluation method.Therefore it uses RBF neural net to design the coal mine safety evaluation model,according to the practical situation of our country’s coal mine.Through the accident tree analysis（FTA）and event tree analysis（ETA）,this paper summarized coal mine accident risk factors and influenced of mine production safety factors.Meanwhile in order to overcome the neural net easy to fall into the local minimum,neural net model of the right value（threshold） is optimized using quantum genetic algorithm.The method is used in fuxin mining company of a subordinate mine.Results show that the model can accurately evaluate coal mine safety in production,for coal mine safety evaluation provided a new way,having important significance for coal mine safety in production.