| EXPERT SYSTEM FOR THE CHARACTERIZATION OF LABORATORY SAMPLE INPUT SIGNALS:AUTOCORR |
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| 作者姓名： | P.MIJLAND J.KLAESSENS B.VANDEGINSTE G.KATEMAN |
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| 作者单位： | Laboratorium voor Analytische Chemie University of Nijmegen，Toernooiveld，NL-6525 ED Nijmegen，The Netherlands，Laboratorium voor Analytische Chemie，University of Nijmegen，Toernooiveld，NL-6525 ED Nijmegen，The Netherlands，Laboratorium voor Analytische Chemie，University of Nijmegen，Toernooiveld，NL-6525 ED Nijmegen，The Netherlands，Laboratorium voor Analytische Chemie，University of Nijmegen，Toernooiveld，NL-6525 ED Nijmegen，The Netherlands |
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| 摘 要： | An expert system is presented for automated time series analysis of laboratory sample input signals.Thesystem,AUTOCORR,builds a model of the time series by identifying the processes that are present.These are an uncorrelated random process and,underlying this,possibly one or more of the following:a first-order autoregressive process,a trend and a periodic process.AUTOCORR has a knowledge baseof 44 rules and 41 facts for this purpose.The employed shell,INFER,allows the use of algorithmicprocedures.Elaborate tests with simulated signals show that AUTOCORR has a very low false positivescore and is successful in describing time series for laboratory simulation models.
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EXPERT SYSTEM FOR THE CHARACTERIZATION OF LABORATORY SAMPLE INPUT SIGNALS:AUTOCORR |
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| P.MIJLAND,J.KLAESSENS,B.VANDEGINSTE,G.KATEMAN.EXPERT SYSTEM FOR THE CHARACTERIZATION OF LABORATORY SAMPLE INPUT SIGNALS:AUTOCORR[J].Journal of Geographical Sciences,1989(2). |
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| Authors: | PMIJLAND JKLAESSENS BVANDEGINSTE GKATEMAN Laboratorium voor Analytische Chemie University of Nijmegen Toernooivel NL- ED Nijmegen The Netherlands |
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| Institution: | P.MIJLAND J.KLAESSENS~ B.VANDEGINSTE G.KATEMAN Laboratorium voor Analytische Chemie,University of Nijmegen,Toernooivel,NL- ED Nijmegen,The Netherlands |
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| Abstract: | An expert system is presented for automated time series analysis of laboratory sample input signals.The system,AUTOCORR,builds a model of the time series by identifying the processes that are present. These are an uncorrelated random process and,underlying this,possibly one or more of the following: a first-order autoregressive process,a trend and a periodic process.AUTOCORR has a knowledge base of 44 rules and 41 facts for this purpose.The employed shell,INFER,allows the use of algorithmic procedures.Elaborate tests with simulated signals show that AUTOCORR has a very low false positive score and is successful in describing time series for laboratory simulation models. |
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| Keywords: | Expert system Time series analysis Autocorrelation Laboratory input signals |
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