摘要
数据校正之前,必须首先确定校正对象的状态,即稳态检验。校正对象的状态通常分为稳态、准稳态和动态3类。目前的稳态检验方法都是"动态"和"稳态"的二值判别模式,只能给出对象状态的定性判断,这在绝大多数实际情况下是不尽合理的。本研究基于模糊集的思想设计了一种新的稳态检验方法,不再将校正对象的状态定性的分为"动态"和"稳态"2类,而是将对象在时域上可能出现的所有状态作为一个模糊集,每一时刻的状态都对应模糊集中的一个具体元素,从而使校正对象的状态有了定量的衡量指标,进而可以显著提高判别的区分度,为数据校正策略提供更加精确的状态参考。
It must be made certain process state before data reconciliation, which is detection of steady state. There are three states of the process： steady state,dynamic state and the transition state. The usual methods for the detection of steady state are all dimorphism detection： dynamic state or steady state. They only give a qualitative detection of the process state and this is inconsequence in most real conditions. In this paper, a new method for the detection of steady state based on fuzzy sets is proposed. It no longer classifies qualitatively the process state only two kinds： dynamic state and steady state. It utilizes fuzzy sets to express process state and quantifies every state of the process u- sing fuzzy formulas. This gives a quantitative weigh index of process state,augments differentiation degree of state detection and provides a more precise state reference for data reconciliation.
出处
《青岛科技大学学报（自然科学版）》
CAS
2010年第1期91-95,共5页
Journal of Qingdao University of Science and Technology:Natural Science Edition
基金
山东省自然科学基金项目(Y2008G14)
上海市基础研究重点项目(08JC1408200)
关键词
数据校正
稳态检验
模糊集
data reconciliation
detection of steady state
fuzzy sets
