摘要
数据动态特征分析与控制技术是一种重要的数据挖掘手段,燃煤锅炉数据具有明显时序性和漂移性等特点,针对目前数据跟踪与监督算法大多缺乏动态性、实时性和稳定性等问题,设计一种基于改进生长神经气模型(GNG)的自适应聚类模型,建立基于概率、范围搜寻、节点平均距离的节点生成、删除机制,实现对漂移数据实时监控.最后以燃煤锅炉动态数据为分析对象进行实验,实验结果表明该模型和算法对动态漂移数据的实时跟踪能力更强,能对燃煤锅炉动态数据进行准确、有效的监督和控制.
Data dynamic feature analysis and control technology are considered as important data mining methods.The coal-fired boiler data have obvious characteristics such as timing and drift.In view of the current lack of dynamic,real-time problems of data tracking monitoring algorithms,we propose an adaptive clustering model based on improved growing neural gas(GNG).The improved GNG is used to realize real time monitoring for drift data.The node generation and deletion mechanism are established.based on probability,range search and node average distance.Finally,the dynamic data for coal-fired boilers are taken as the analysis object.The experimental results show that the model and algorithm have stronger real-time tracking ability for dynamic drift data,and can effectively supervise and control the dynamic data of coal-fired boilers.
作者
吴永明
陈琳升
李少波
WU Yong-ming;CHEN Lin-sheng;LI Shao-bo(Key Laboratory of Advanced Manufacturing Technology of Ministry of Education,Guizhou University,Guiyang 550025,China;Sate Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China;College of Mechanical Engineering,Guizhou University,Guiyang 550025,China)
出处
《控制与决策》
EI
CSCD
北大核心
2021年第8期1855-1861,共7页
Control and Decision
基金
国家自然科学基金项目(51505094,61962009)
贵州省科学技术基金计划项目[(2016)1037]
贵州省科技支撑计划项目[(2017)2029]
贵州大学引进人才科研项目[贵大人基合字(2014)60号]。
关键词
燃煤锅炉
动态特征
数据漂移
GNG
监控
coal-fired boiler
dynamic characteristics
data drift
GNG
monitor
作者简介
吴永明(1982-),男,教授,博士生导师,从事数据挖掘、制造系统自动化等研究,E-mail:wu20811055@163.com;通讯作者:陈琳升(1994-),男,硕士生,从事数据挖掘的研究,E-mail:clsnnqns@163.com;李少波(1973-),男,教授,博士生导师,从事计算设计、制造大数据与制造信息系统等研究,E-mail:lishaobo@gzu.edu.cn.
