基于小波神经网络矿山安全的评价模型

煤矿是一个多工序、多环节、生产过程复杂、时空变化大、环境恶劣的生产企业,其安全系统是一典型的非线性系统,对矿山进行安全评价是当前安全管理中的一个重要环节.采用由伸缩和平移因子决定的小波基函数代替Sigmoid等传递函数,选用23项指标作为输入节点,建立矿山安全的小波神经网评价模型,该模型可自动确定网络参数,避免了传统神经网络需要人为干预网络结构参数的不足.实例分析表明,提出的WNN网络的评价绝对误差平均为0.425%,而BP网络评价绝对误差平均为3.1%.这说明,WNN网络泛化能力远好于BP网络,该模型具有重要的应用价值.

Wavelet Neural Network Estimation Model for Mine Safety

Multiple procedures/sections,complex operation process,abrupt time/space change and severe working conditions feature are in the production of a coal mine.So,its safety system is typically a nonlinear one,and the safety estimation of a coal mine is inevitably of more and more importance nowadays in China.A safety estimation model is therefore developed via wavelet neural network,where the primary function of wavelet,which is based on retract and translation factors,is introduced instead of sigmoid transfer function,etc.,with 23 indices picked out as input nodes.The model is able to make certain of network parameters automatically,thus avoiding the trouble in which the artificial intervention is needed to modify the structural parameters of network if using conventional neural network.It is exemplified that the mean absolute error of the estimation results via WNN network as proposed is 0.425%,while that via BP network is up to 3.1%.It means that the generalizability of WNN is much better than that of BP.So,the model is highly applicable.