The prediction algorithm of the technical state of an object by means of fuzzy logic inference models
The prediction algorithm of the technical state of an object was elaborated to predict emergencies at object functioning and to provide urgent steps required to control the object. The technical state is described by a set of indices represented by a system of time series. The adequate mathematical...
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| Vydáno v: | Procedia engineering Ročník 201; s. 767 - 772 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
2017
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| Témata: | |
| ISSN: | 1877-7058, 1877-7058 |
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| Abstract | The prediction algorithm of the technical state of an object was elaborated to predict emergencies at object functioning and to provide urgent steps required to control the object. The technical state is described by a set of indices represented by a system of time series. The adequate mathematical model is built by means of an adaptive regression modeling for the system of time series. The model is used to predict the object indices. The predicted values of indices are analyzed by means of fuzzy logic and predicted state of an object is deduced as a fuzzy term. |
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| AbstractList | The prediction algorithm of the technical state of an object was elaborated to predict emergencies at object functioning and to provide urgent steps required to control the object. The technical state is described by a set of indices represented by a system of time series. The adequate mathematical model is built by means of an adaptive regression modeling for the system of time series. The model is used to predict the object indices. The predicted values of indices are analyzed by means of fuzzy logic and predicted state of an object is deduced as a fuzzy term. |
| Author | Kuvayskova, Yu. E. |
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| Keywords | prediction technical object adaptive regression modeling fuzzy logic system of time series |
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| References | Klyachkin, Kuvayskova, Alyoshina, Kravtsov (bib0001) 2013; 15 Valeev, Kuvaiskova (bib00016) 2007; 2 Klyachkin, Kuvayskova, Bubyr (bib0002) 2015; 6 Valeev, Kuvayskova (bib00014) 2006; 4 A.V. Leonenkov, Fuzzy modeling in MATLAB and fuzzyTECH environment, BHV–Petersburg, St. Petersburg, 2003. Engle (bib00015) 1982; 50 Pavlov, Anishchenko (bib0009) 2007; 50 Kuvayskova, Fedorova (bib0007) 2016; 1 Kuvayskova, Fedorova, Zhukov (bib0006) 2016; 1 Klyachkin, Kuvayskova, Alёshina (bib00017) 2014; 1 S.G. Valeev, Regression modeling in data processing, second ed., FEN, Kazan, 2001. Kuvayskova, Zhukov (bib00011) 2015; 2 George (bib00013) 2015 Kuvayskova (bib0008) 2013; 15 Krasheninnikov, Kuvayskova (bib00012) 2016; 9 Kuvaiskova, Aleshina (bib0004) 2016; 4 Kuvayskova, Fomenko (bib00010) 2015 Klyachkin (10.1016/j.proeng.2017.09.624_bib0001) 2013; 15 10.1016/j.proeng.2017.09.624_bib0005 Valeev (10.1016/j.proeng.2017.09.624_bib00016) 2007; 2 Kuvayskova (10.1016/j.proeng.2017.09.624_bib00010) 2015 George (10.1016/j.proeng.2017.09.624_bib00013) 2015 Kuvaiskova (10.1016/j.proeng.2017.09.624_bib0004) 2016; 4 Kuvayskova (10.1016/j.proeng.2017.09.624_bib00011) 2015; 2 Klyachkin (10.1016/j.proeng.2017.09.624_bib00017) 2014; 1 Kuvayskova (10.1016/j.proeng.2017.09.624_bib0007) 2016; 1 Valeev (10.1016/j.proeng.2017.09.624_bib00014) 2006; 4 Klyachkin (10.1016/j.proeng.2017.09.624_bib0002) 2015; 6 Engle (10.1016/j.proeng.2017.09.624_bib00015) 1982; 50 10.1016/j.proeng.2017.09.624_bib0003 Kuvayskova (10.1016/j.proeng.2017.09.624_bib0006) 2016; 1 Pavlov (10.1016/j.proeng.2017.09.624_bib0009) 2007; 50 Krasheninnikov (10.1016/j.proeng.2017.09.624_bib00012) 2016; 9 Kuvayskova (10.1016/j.proeng.2017.09.624_bib0008) 2013; 15 |
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