摘要
针对矿井通风机系统参数难以测定和故障率较高的特点,提出将多层前向传递神经网络应用于通风机系统控制的方法,利用BP算法训练神经网络,从而实现矿井通风机数据的实时监测及系统的故障诊断并报警。该控制算法对被控对象的数学模型依赖程度较低,为非线性系统的控制提供了一种行之有效的研究方法。
This paper is directed at the characteristics of parameters more difficult to determine and faults more likely to occur in the mine ventilator system. The back-propagation neural network is applied to ventilator system. Data real-time observation and fault diagnosis for ventilator system are realized by training the neural network using BP algorithm. The paper presents a novel learning linear control mechanism for a class of nonlinear system, which shows less dependence on the model of non-linear control system.
出处
《黑龙江科技学院学报》
CAS
2004年第6期347-349,共3页
Journal of Heilongjiang Institute of Science and Technology
关键词
神经网络
通风机
故障诊断
neural network
ventilator
fault diagnosis
