摘要
主通风机切换过程中,取压风量测量作为监测井下供给风量的主要手段,是矿井主扇通风系统安全、稳定与经济运行的重要保障.然而,由于取压孔极易出现堵塞现象,需要频繁维护,导致无法实时测量井下供给风量,难以实现主通风机切换过程的闭环优化控制.同时,随着隐含层节点数的增加,基于随机配置网络(Stochastic configuration network,SCN)的估计模型存在过拟合和泛化能力差的缺点.为了解决上述问题,结合正则化(Regularization,R)技术,本文提出一种新型的改进SCN算法,即RSC算法,用于井下供给风量的建模.基准回归分析和工业实验表明:与SCN方法相比,建立的RSC模型具有较高的模型精度和较好的泛化性能.
In a main fan switchover process(MFSP),airflow quantity measuring by pressure drop across a duct is the main means for monitoring the distribution of underground airflow quantity(UAQ).It ensures the safe,stable and economic operation of the mine main fan ventilation system.However,as the pressure port is prone to blocking and requires frequent maintenance,it is impossible to realize the real-time measurement of UAQ and closed-loop optimal control of MFSP.Meanwhile,with the increase of number of hidden nodes,the estimation model based on stochastic configuration network(SCN)has the disadvantages of overfitting and poor generalization ability.To solve the above problems,this paper develops a novel improved SCN algorithm by incorporating the regularization(R)technique called RSC algorithm.Benchmark regression analysis and industrial tests show that compared with SCN,the established RSC model possesses higher model accuracy and better generalization ability.
作者
王前进
杨春雨
马小平
张春富
彭思敏
WANG Qian-Jin;YANG Chun-Yu;MA Xiao-Ping;ZHANG Chun-Fu;PENG Si-Min(School of Electrical Engineering,Yancheng Institute of Technology,Yancheng 224051;School of Information and Control Engineering,China University of Mining and Technology,Xuzhou 221116)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2021年第8期1963-1975,共13页
Acta Automatica Sinica
基金
国家自然科学基金(61873272,61603392)
江苏省自然科学基金(BK20191043)
江苏省“双创团队”项目(2017)
盐城工学院校级科研项目(xjr2019018)资助。
关键词
主通风机
切换过程
井下供给风量
随机配置网络
正则化
Main fan
switchover process
underground airflow quantity
stochastic configuration network
regularization
