欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
由于采煤工作面瓦斯抽采率影响因素复杂多样,且各影响因素之间存在着动态、模糊的非线性关系,传统的预测方法难以建立其预测模型。神经网络具有自组织、自适应、并行处理等特性和很强的非线性逼近能力,通过采煤工作面瓦斯抽采率和其影响因素之间的函数关系建立了预测模型。结果表明,预测模型精度能够满足要求,具有合理性和可行性。
英文摘要：
As influencing factors of gas extraction rate at coal face are complicated,nonlinear relation which are dynamic and vague exist between each influencing factors,prediction model is hard to establish on traditional prediction methods.Neural network has the characteristics of self-organization,self-adaptive,parallel processing and strong nonlinear approximation ability.The prediction model was established based on functional relationship between gas extraction rate at coal face and its influencing factors.The result indicates that the precision of the prediction model could meet the request,and the model is reasonable and feasible.
同期刊论文项目
同项目期刊论文
Experimental study on properties of transverse strain and gas flow of coal containing gas in process
Experimental study on evolution of mechanical parameters of coal containing gas under unloading conf
位置：