• CN:11-2187/TH
  • ISSN:0577-6686

›› 2005, Vol. 41 ›› Issue (1): 135-139.

• 论文 • 上一篇    下一篇

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制造系统的非统计调整与误差预测

夏新涛;王中宇;朱坚民;李旭东   

  1. 河南科技大学机电工程学院;洛阳轴承集团有限公司工艺处
  • 发布日期:2005-01-15

REGULATION AND ERROR FORECAST OF MANUFACTURE SYSTEM USING NON-STATISTICAL THEORY

Xia Xintao;Wang Zhongyu;Zhu JianminLi Xudong   

  1. College of Mechanical and Electronical Engineering, Henan University of Science and Technology Technology Department, Luoyang Bearing Group Ltd
  • Published:2005-01-15

摘要: 制造系统的初始调整经常采用统计调整法。统计调整法以概率论和统计理论为基础,要求制造系统的输出具有正态分布,而且标准差和包含因子事先确知,难以解决非正态分布、概率分布与标准差以及包含因子等信息未知或信息不全面的制造系统的调整与误差预测问题。为此,提出一种基于模糊集合理论的新调整方法——非统计调整法。非统计调整法可以研究正态分布、均匀分布和混合分布等多种典型和非典型分布问题,不必知道标准差和包含因子等特征参数就可以直接估计出系统输出的参数分散区间。对系统数据的初始处理和最终处理采用模糊区间数的运算法则,同时考虑区间数的平均值和极差,可以更全面、准确地描述随机误差大小和方向的变化对系统输出的影响。计算机仿真和生产案例研究表明,非统计调整法对系统输出的估计误差很小,准确率很高。

关键词: 调整, 非统计理论, 模糊集合理论, 误差, 预测, 制造系统

Abstract: At present, a manufacture system is regulated using the statistical method. The statistical method is based on probability and statistics theory. Normal distribution is required, the standard deviation and the coverage factor are beforehand certain for the output of the manufacture system, and therefore it will be very difficult to resolve the problems of the Regulation and error forecast of the manufacture system with non-normal distributions, unknown or non-comprehensive information by means of the classical statistical theory. Therefore, a novel method is proposed. This method is the non-statistical method based on fuzzy-set theory. By using this method, many typical and untypable problems, such as normal distribution, uniform distribution, and commingle distributions, can be researched, the disperse interval of the systemic output can be straightway estimated without any known characteristic parameter such as the standard deviation and the coverage factor. Influence of the random error’s change in size and direction on the systemic output can be roundly and truly described because the algorithm of fuzzy interval-data is introduced, and the average values and the maximum difference-values of fuzzy interval-data are considered in processing the commence and conclude data. Computer imitation and manufacture cases indicate the very small error and the very high veracity of estimating for the systemic output using the non-statistical method proposed.

Key words: Error, Forecast, Fuzzy-set theory, Manufacture system, Non-statistical theory, Regulation

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