基于BP神经网络的水力旋流器建模仿真研究  

Research on Model Simulation of Hydrocyclone Based on BP Neural Network

作　　者：张婧[1] 徐文尚[1] 盖文东[1] 于庆明[1] 于振波[1] 

机构地区：[1]山东科技大学信息与电气工程学院,山东青岛266510

出　　处：《煤矿机械》2008年第9期56-58,共3页Coal Mine Machinery

摘　　要：通过对某一固定的水力旋流器自身工艺的研究,确定水力旋流器的输入与输出。分2种情况对有相互影响的输入参数分别建立BP神经网络的模型和利用Matlab工具箱中的BP神经网络建立水力旋流器的三层神经网络模型。通过对收集的该设备的实例数据进行仿真训练,结果表明不仅是旋流器本身参数,调浆槽的液位等也会对旋流器效率产生影响。Based on the research of a fixed hydrocyclone technics, definite the inputs and outputs of hydrocyclone. On the foundation of the input parameters with mutual influence divide them into two different situations to establish separately the model of BP NN（Neural Network） and use the BP neural network toolbox in Matlab to establish hydroeyclone three neural network model. Carrying on the simulation training of the equipment＇s collected example data, the result can indicate that not only the hydroeyclone parameters, but also the level of tank have the influence to the efficiency of hydroeyclone.

关 键 词：水力旋流器 BP神经网络 模型 样本训练 

分 类 号：TD462[矿业工程—矿山机电] TP391[自动化与计算机技术—计算机应用技术]