摘要
煤与瓦斯突出是一种极其复杂的瓦斯动力灾害现象。以突出前兆的非线性特征值为输入值,基于BP神经网络的煤与瓦斯突出非线性预测模型,可以智能化定量判识煤与瓦斯突出危险。自适应学习速率法加快了网络收敛速度,该模型通过Matlab工具实现。实验结果表明,基于BP神经网络的预测模型可靠,预测精度高,效果良好。
Coal and gas outburst is a very complex dynamic gas disaster.A non-linear predicting model in which the input value is the value of non-linear characteristics before outburst portent,based on BP neural network,can intelligently quantitatively identify coal and gas outburst danger.Adaptive learning rate method can speed up the convergence rate of the network and the model is completed through the Matlab tool.The experimental result reveal that the prediction model based on BP neural network is reliable and has accurate results.
出处
《实验室研究与探索》
CAS
北大核心
2009年第10期48-50,共3页
Research and Exploration In Laboratory
基金
黑龙江省教育厅科学技术研究项目(11531332)
关键词
BP神经网络
煤与瓦斯突出
预测
评价
back-propagation（BP） neural network
coal and gas outburst
predication
evaluation
