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PATTERN CONCEPTION METHOD AND DEVICE OF ARRAY GAS SENSOR

机译:阵列式气体传感器的模式概念方法和装置

摘要

The present invention relates to a pattern recognition method and apparatus of a gas sensor array in order to improve the difficulties and on the signal processing speed is determined by the deviation of the sensor signal.; The present invention initializes the category data to allow to reduce the determined error by classifying as a separate category of non-existing category for not learning, at the same time as measurements to allow for discrimination, or for the gas to the unknown, to read the input signals in each category seeking a probability included in, and the probability that form both the reference value or less is a new category, and classify input data into categories with which the probability of any of highest probability is more than the reference value, this category including the classification data obtained in the asking for the variation coefficient, the coefficient of variation to form a new category is above the reference value, seeking the distribution of the variation coefficient of the reference value or less if the corresponding category, and distributed to form a large, new category than the standard dispersion, if the variance is less than the standard deviation in this category The flow, and it will determine which improves the speed determined by the reformation display data after the probability that the different categories of all the data, to reduce the determined error.
机译:本发明涉及一种气体传感器阵列的模式识别方法和装置,以提高难度,并根据传感器信号的偏差确定信号处理速度。本发明通过分类为不学习的不存在的类别的单独类别,同时进行测量以允许辨别或允许未知气体阅读,从而初始化类别数据以减少确定的误差。每个类别中的输入信号都寻求一个包含在其中的概率,并且构成参考值或小于参考值的概率是一个新类别,并将输入数据分类为最大概率大于参考值的类别,该类别包括在求出变异系数时获得的分类数据,形成新类别的变异系数高于参考值,如果对应的类别寻求参考值的变异系数的分布以下,则进行分布如果方差小于此类别中的标准偏差,则形成一个比标准分散体大的新类别y流程,它将确定在提高所有数据的不同类别的概率之后,由改良显示数据确定的速度提高了,从而减少了确定的误差。

著录项

  • 公开/公告号KR0132477B1

    专利类型

  • 公开/公告日1998-04-21

    原文格式PDF

  • 申请/专利权人 LG ELECTRONICS CO.LTD;

    申请/专利号KR19940034366

  • 发明设计人 HONG HYUNG-GI;

    申请日1994-12-15

  • 分类号H04B1/18;

  • 国家 KR

  • 入库时间 2022-08-22 02:48:45

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