改进粒子群神经网络在煤矿安全评价中的应用  

Application of Improved Particle-Swarm-Optimization Neural Network in Coalmine Safety Evaluation

作　　者：何荣军[1] 骆大勇[1] HE Rongjun;LUO Dayong(Chongqing Vocational Institute of Engineering Chongqing 402260)

机构地区：[1]重庆工程职业技术学院,重庆402260

出　　处：《工业安全与环保》2018年第11期29-31,共3页Industrial Safety and Environmental Protection

基　　金：国家自然科学基金(51474206)

摘　　要：基于改进粒子群的神经网络基本原理,综合考虑煤矿安全评价的各项指标,建立了一个综合型的评价模型。该模型的方法以神经网络为基础,用改进粒子群算法来寻找最佳平衡的惯性权值,提高神经网络训练速度和精度。实践证明,模型具有改进粒子群算法的全局寻优能力和神经网络的广泛映照能力,有很强的预测能力,为煤矿安全评价提供了重要的途径。Based on the principle of improved particle swarm optimization neural network,a comprehensive evaluation model is established by taking into account the various indexes of coal mine safety evaluation.The method is based on neural networks,and uses improved particle swarm optimization(PSO)to find the optimal balance weight,and to improve the convergence speed and accuracy of the neural network.Practice has proved that the model has the ability of global optimization of the improved particle swarm optimization algorithm and the extensive mapping ability of the neural network,also has a strong prediction ability,which provides an important way for coal mine safety evaluation.

关 键 词：煤矿安全评价 改进粒子群算法 神经网络 

分 类 号：TD79[矿业工程—矿井通风与安全]