期刊文献+

FUZZY ECCENTRICITY AND GROSS ERROR IDENTIFICATION 被引量:1

FUZZY ECCENTRICITY AND GROSS ERROR IDENTIFICATION
下载PDF
导出
摘要 The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollance relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function ofgruss error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found, utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s. The dominant and recessive effect made by exceptional interferer is analyzed in measurement system based on responsive character, and the gross error model of fuzzy clustering based on fuzzy relation and fuzzy equipollance relation is built. The concept and calculate formula of fuzzy eccentricity are defined to deduce the evaluation rule and function ofgruss error, on the base of them, a fuzzy clustering method of separating and discriminating the gross error is found, utilized in the dynamic circular division measurement system, the method can identify and eliminate gross error in measured data, and reduce measured data dispersity. Experimental results indicate that the use of the method and model enables repetitive precision of the system to improve 80% higher than the foregoing system, to reach 3.5 s, and angle measurement error is less than 7 s.
出处 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2006年第1期143-145,共3页 中国机械工程学报(英文版)
基金 This project is supported by National Natural Science Foundation of China (No.59575081,No.59735120).
关键词 Fuzzy clustering Gross error model Fuzzy eccentricity Repetitive precision improvement Fuzzy clustering Gross error model Fuzzy eccentricity Repetitive precision improvement
  • 相关文献

参考文献6

  • 1TSUEN H H.An application of fuzzy clustering in group-positioning analysis[C]//Proc.Natl.Sci.Counc.ROC(C),2000,10(2):157-167.
  • 2ZAIDI H,DIAZ G M,BOUDRAA A,et al.Fuzzy clustering-based segmented attenuation correction in whole-body PET imaging[J].Phys.Med.Biol,2002,47:1143-1160.
  • 3YE Bing,FEI Yetai.Forecasting model of Dynamic error and precision melioration of measurement system[J].Precision Mechanical Measurement,2002,8(4):153-157.
  • 4GOVAERT G,NADIF M.Clustering with block mixture models[J].Pattern Recognition Journal,2003,36:463-473.
  • 5HALL L O,OZYURT I B,BEZDEK J C.Clustering with a genetically optimized approach[J].IEEE Trans.Evol.Computer,1999,3(2):103-112.
  • 6YE Bing,FEI Yetai.Error forecast and model optimization of optic measurement system[J].Optical design and testing (SPIE),2002,4927:722-725.

同被引文献17

  • 1QINGMing,CAOYue,HUANGTian-min.Fuzzy Entropy: Axiomatic Definition and Neural Networks Model[J].Chinese Quarterly Journal of Mathematics,2004,19(3):319-323. 被引量:1
  • 2梅晓锋,辛小龙.模糊熵,距离测度和散度测度(英文)[J].模糊系统与数学,2006,20(1):56-61. 被引量:7
  • 3何永勇,印欣运,褚福磊.基于小波尺度谱的转子系统碰摩声发射特性[J].机械工程学报,2007,43(6):149-153. 被引量:17
  • 4GREEN A. Characteristics of acoustic emission response from materials[J]. Japan Acoustic Emission Symposium, 1992: 232-235.
  • 5SACHSE W, GRABEC I. Intelligent processing of acoustic emission signals[J]. Journal of Materials Evaluation, 1992, 50(7): 826-854.
  • 6WANG Q, MATHEW J. Fault detection and diagnosis in low speed rolling element bearings. Part II. The use of parametric spectra[J]. Mech. Syst. Signal Processing, 1992, 6(4): 297-307.
  • 7BAMBA E, NAKAZATO K. Fuzzy theoretical interactions between consciousness and emotions[C]// Proceedings of 9th IEEE International Workshop on Robot and Human Interactive Communication RO-MAN 2000, Sept. 27-29, 2000, Osaka, Japan. 2000: 218-223.
  • 8REYNOLDS D A. Speaker identification and verification using Gaussian mixture speaker models[J]. Speech Communication, 1995, 17(1): 91-108.
  • 9COLOMBI J, RUCK D, ROGERS S, et al. Cohort selection and word grammar effects for speaker recognition[C]//Proc. IEEE Int. Conf. Acoustics, Speech and Signal Processing, Atlanta, 1996. 85-88.
  • 10万晖,杜凯.一类拟模糊熵的渐近界[J].西北大学学报(自然科学版),2007,37(5):701-704. 被引量:1

引证文献1

二级引证文献12

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部