摘要
状态预测是厂房温湿度检测仪器预防性维护的重要组成部分,也是提高厂房温湿度检测仪器使用寿命的有效措施。本文针对BP神经网络在设备状态预测中的不足,提出基于BP神经网络的L-M优化算法,用以对厂房温湿度检测仪器预测中。通过仿真和实验相结合的方法,采用原始样本数据对厂房温湿度检测仪器进行预测。研究表明,基于BP神经网络的L-M优化算法能够实现对厂房温湿度检测仪器的状态预测,并且误差较小,证明了该方法的有效性。
State prediction is an important component of preventive maintenance of plant temperature and humidity measuring instruments. It is also an effective measure to improve the service life of temperature and humidity measuring instruments. In this paper,a L-M optimization algorithm based on BP neural network is proposed according to the deficiency of BP neural network in equipment state prediction. Through the combination of simulation and experiment, the original sample data is used to predict the temperature and humidity of the building. The research shows that the L-M optimization algorithm based on BP neural network can realize the state prediction of the temperature and humidity detection instrument of the factory building, and the error is small, which proves the validity of the method.
作者
徐志浩
舒梦
林翌臻
王贤芬
励丽
孙建国
张庆俊
XU Zhihao;SHU Meng;LIN Yizhen;WANG Xianfen;LI Li;SUN Jianguo;ZHANG Qingjun(Zhejiang Zhongyan Industry Co.,Ltd.,Ningbo Zhejiang,315504)
出处
《自动化与仪器仪表》
2018年第11期97-99,共3页
Automation & Instrumentation
关键词
状态预测
BP神经网
厂房温湿度检测仪器
L-M优化算法
state prediction
BP neural network
building temperature and humidity detector
L-M optimization algorithm
