Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas
The problem of the evaluation of fuzzy situations arises quite often in real life. Images, symbols, signals and so on usually are fuzzily described and can be considered as fuzzy situations. So the parameters, which characterize the fuzzy situation, are not only numerical ones, but verbal too, for e...
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Online Access: | http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20060606_114125-89567/DS.005.0.02.ETD |
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ndltd-LABT_ETD-oai-elaba.lt-LT-eLABa-0001-E.02~2006~D_20060606_114125-895672013-11-16T03:58:33Z2006-06-06litInformaticsNaujokas, ŽydrūnasMiglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimasResearch and development of the module for the evaluation of fuzzily characterized situationLithuanian Academic Libraries Network (LABT)The problem of the evaluation of fuzzy situations arises quite often in real life. Images, symbols, signals and so on usually are fuzzily described and can be considered as fuzzy situations. So the parameters, which characterize the fuzzy situation, are not only numerical ones, but verbal too, for example, an object can be described as "heavy, little and moves fast". It is difficult to decide something formally about the object with such characterization. Leasing companies meet this problem in contracts classification process too. Accordingly most leasing companies have own contracts classification (recognition) systems, which mostly are statistically based and the similarities and differences between good and bad contracts are not computed. Therefore the demand arises to develop the intellectual recognition system using fuzzy logic. This system should be simply integrating in any leasing company. It also must compute and measure the similarities and differences between good and bad contracts from their evaluation history. Such intellectual system could be as an adviser for business expert. The module of fuzzily characterized situation's evaluation is implemented using: 1) fuzzy sets, 2) fuzzy clustering, 3) fuzzy recognition. The general idea was experimentally investigated using artificially generated data as well as using data from real contracts. The software developed during this research is under preparation to be integrated in companies "Baltic... [to full text]Fuzzy logicMiglotoji logikaMaster thesisMaciulevičius, StasysJasinevičius, RaimundasTelksnys, LaimutisMockus, JonasBarauskas, RimantasPranevičius, HenrikasPlėštys, RimantasŽvironas, ArūnasKaunas University of TechnologyKaunas University of Technologyhttp://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20060606_114125-89567LT-eLABa-0001:E.02~2006~D_20060606_114125-89567KTU-LABT20060606-114125-89567http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20060606_114125-89567/DS.005.0.02.ETDUnrestrictedapplication/pdf |
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Lithuanian |
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Dissertation |
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Informatics Fuzzy logic Miglotoji logika |
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Informatics Fuzzy logic Miglotoji logika Naujokas, Žydrūnas Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
description |
The problem of the evaluation of fuzzy situations arises quite often in real life. Images, symbols, signals and so on usually are fuzzily described and can be considered as fuzzy situations. So the parameters, which characterize the fuzzy situation, are not only numerical ones, but verbal too, for example, an object can be described as "heavy, little and moves fast". It is difficult to decide something formally about the object with such characterization. Leasing companies meet this problem in contracts classification process too. Accordingly most leasing companies have own contracts classification (recognition) systems, which mostly are statistically based and the similarities and differences between good and bad contracts are not computed. Therefore the demand arises to develop the intellectual recognition system using fuzzy logic. This system should be simply integrating in any leasing company. It also must compute and measure the similarities and differences between good and bad contracts from their evaluation history. Such intellectual system could be as an adviser for business expert.
The module of fuzzily characterized situation's evaluation is implemented using:
1) fuzzy sets,
2) fuzzy clustering,
3) fuzzy recognition.
The general idea was experimentally investigated using artificially generated data as well as using data from real contracts. The software developed during this research is under preparation to be integrated in companies "Baltic... [to full text] |
author2 |
Maciulevičius, Stasys |
author_facet |
Maciulevičius, Stasys Naujokas, Žydrūnas |
author |
Naujokas, Žydrūnas |
author_sort |
Naujokas, Žydrūnas |
title |
Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
title_short |
Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
title_full |
Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
title_fullStr |
Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
title_full_unstemmed |
Miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
title_sort |
miglotai apibrėžtos situacijos įvertinimo modulio sudarymas ir tyrimas |
publisher |
Lithuanian Academic Libraries Network (LABT) |
publishDate |
2006 |
url |
http://vddb.library.lt/fedora/get/LT-eLABa-0001:E.02~2006~D_20060606_114125-89567/DS.005.0.02.ETD |
work_keys_str_mv |
AT naujokaszydrunas miglotaiapibreztossituacijosivertinimomoduliosudarymasirtyrimas AT naujokaszydrunas researchanddevelopmentofthemodulefortheevaluationoffuzzilycharacterizedsituation |
_version_ |
1716614572185485312 |