[1]王刚,张博,王冠,等. 模糊系统结合最小二乘的温升预测方法[J].哈尔滨理工大学学报,2017,(06):52-56.[doi:10. 15938 /j. jhust. 2017. 06. 010]
 WANG Gang,ZHANG Bo,WANG Guan,et al. The Method of Predicting the Rise of Temperature byCombining Fuzzy System and Recursive Least Square[J].哈尔滨理工大学学报,2017,(06):52-56.[doi:10. 15938 /j. jhust. 2017. 06. 010]
点击复制

 模糊系统结合最小二乘的温升预测方法()
分享到:

《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

卷:
期数:
2017年06期
页码:
52-56
栏目:
材料科学与工程
出版日期:
2017-12-25

文章信息/Info

Title:
 The Method of Predicting the Rise of Temperature by
Combining Fuzzy System and Recursive Least Square
文章编号:
1007- 2683( 2017) 06- 0052- 05
作者:
 王刚 张博 王冠 叶三排
 ( 平高集团有限公司技术中心,河南平顶山467001)
Author(s):
 WANG Gang ZHANG Bo WANG Guan YE San-pai
 ( Technology Center,Pinggao Group Co. ,Ltd. ,Pingdingshan 467001,China
关键词:
 金具 温升试验 模糊系统 回归模型
Keywords:
 fitting the experiment of the rise of temperature fuzzy system regression analysis model
分类号:
TM11; TP18
DOI:
10. 15938 /j. jhust. 2017. 06. 010
文献标志码:
A
摘要:
 针对金具温升过高现象,提出了新的温升预测方法。通过温升试验得到训练数据与测
试数据,利用训练数据通过递推最小二乘结合遗传算法的方法对模糊系统进行训练,利用测试数据
对训练后的模型进行检验,误差处于合理范围。训练与测试结果说明运用新方法预测金具温升是
可行的。通过回归分析对金具温升进行预测,并与新方法进行比较,新方法的预测效果优于传统的
回归模型,比较结果体现了新方法在温升预测方面的优势。
Abstract:
 For the problem that the rise of temperature of fitting is too high to control,a new method of
predicting the rise of temperature has been put forward. The training data and the testing data are obtained from the
experiment of rising of temperature. Through training data,the fuzzy system is trained by recursive least square
combined genetic algorithm. Then the model is tested by testing data. The error is in reasonable range. The result
shows that the new method is feasible to predict the rise of temperature of connection fitting. Regression analysis is
used to predict the rise of temperature of connection fitting and the result is compared with that of new model,the
result of new model is better than that of traditional regression analysis model,the result of comparison reflects that
the new method has advantages for predicting the rise of temperature.

相似文献/References:

[1]孙永全,郭建英,陈洪科,等.AMSAA模型可靠性增长预测方法的改进[J].哈尔滨理工大学学报,2010,(05):49.
 SUN Yong-quan,GUO Jian-ying,CHEN Hong-ke,et al.An Improved Reliability Growth Prediction Algorithm Based on AMSAA Model[J].哈尔滨理工大学学报,2010,(06):49.
[2]滕志军,李晓霞,郑权龙,等.矿井巷道的MIMO信道几何模型及其信道容量分析[J].哈尔滨理工大学学报,2012,(02):14.
 TENG Zhi-jun,LI Xiao-xia,ZHENG Quan-long.Geometric Model for Mine MIMO Channels and Its Capacity Analysis[J].哈尔滨理工大学学报,2012,(06):14.
[3]李艳苹,张礼勇.新训练序列下的改进OFDM符号定时算法[J].哈尔滨理工大学学报,2012,(02):19.
 LI Yan-ping,ZHANG Li-yong.An Improved Algorithm of OFDM Symbol Timing Based on A New Training Sequence[J].哈尔滨理工大学学报,2012,(06):19.
[4]赵彦玲,车春雨,铉佳平,等.钢球全表面螺旋线展开机构运动特性分析[J].哈尔滨理工大学学报,2013,(01):37.
 ZHAO Yan-ling,CHE Chun-yu,XUAN Jia-ping,et al.[J].哈尔滨理工大学学报,2013,(06):37.
[5]李冬梅,卢旸,刘伟华,等.一类具有连续接种的自治SEIR传染病模型[J].哈尔滨理工大学学报,2013,(01):73.
 LI Dong-mei,LU Yang,LIU Wei-hua.[J].哈尔滨理工大学学报,2013,(06):73.
[6]华秀英,刘文德.奇Hamiltonian李超代数偶部的非负Z-齐次导子空间[J].哈尔滨理工大学学报,2013,(01):76.
 HUA Xiu-ying,LIU Wen-de.[J].哈尔滨理工大学学报,2013,(06):76.
[7]桂存兵,刘洋,何业军,等.基于LCC谐振电路阻抗匹配的光伏发电最大功率点跟踪[J].哈尔滨理工大学学报,2013,(01):90.
 GUI Cun-bing,LIU Yong,HE Ye-jun.[J].哈尔滨理工大学学报,2013,(06):90.
[8]翁凌,闫利文,夏乾善,等.PI/TiC@Al2O3复合薄膜的制备及其电性能研究[J].哈尔滨理工大学学报,2013,(02):25.
 WENG Ling,YAN Li-wen,XIA Qian-shan.[J].哈尔滨理工大学学报,2013,(06):25.
[9]姜彬,林爱琴,王松涛,等.高速铣刀安全性设计理论与方法[J].哈尔滨理工大学学报,2013,(02):63.
 JIANG Bin,LIN Ai-qin,WANG Song-tao,et al.[J].哈尔滨理工大学学报,2013,(06):63.
[10]李星纬,李晓东,张颖彧,等.EVOH 磺酸锂电池隔膜的制备及微观形貌[J].哈尔滨理工大学学报,2013,(05):18.
 LI Xing- wei,LI Xiao- dong,ZHANG Ying- yu,et al.The Preparation and Microcosmic Morphology oEVOH- SO Li Lithium Ion Battery Septum[J].哈尔滨理工大学学报,2013,(06):18.

更新日期/Last Update: 2018-03-10