32 research outputs found
SELECTION OF THE BEST CONSULTANT FOR SAP ERP PROJECT USING COMBINED AHP-IBA APPROACH
Abstract: In this paper we propose a combined AHP-IBA model for selecting the best SAP consultant for an SAP ERP project. The goal of the SAP Project Manager is to choose the best consultant, the one who is able to implement standard SAP functionalities with quality and on time.When making a decision on the basis of multiple criteria, the traditional Analytic Hierarchy Process (AHP) method does not take into account the fact that attributes may correlate, assuming that there are no dependencies between them. However, the dependencies of the attributes can often be used to model important knowledge for multiple criteria decision analysis. We propose an extension to the traditional AHP method by applying Interpolative realization of Boolean algebra (IBA), using AHP to determine the criteria weights, and IBA to model the logical interactions among criteria. The research conducted on ERP consultant selection suggests that the decision making process is modelled more accurately if logical interactions between attributes are modelled before applying AH
A logic-based approach to similarity modeling
Π£ ΠΎΠ²ΠΎΡ Π΄ΠΎΠΊΡΠΎΡΡΠΊΠΎΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΡΠ²Π΅Π΄Π΅Π½ ΡΠ΅ Π»ΠΎΠ³ΠΈΡΠΊΠΈ ΠΏΡΠΈΡΡΡΠΏ ΠΌΠΎΠ΄Π΅Π»ΠΎΠ²Π°ΡΡ ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ ΠΊΠΎΡΠΈ ΡΠ΅ Π·Π°ΡΠ½ΠΎΠ²Π°Π½ Π½Π° ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»Π°ΡΠΈΠ²Π½ΠΎΡ ΠΡΠ»ΠΎΠ²ΠΎΡ Π°Π»Π³Π΅Π±ΡΠΈ. ΠΠ° ΠΌΠ΅ΡΠ΅ΡΠ΅ ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π΅ ΡΡ Π½ΠΎΠ²Π΅ ΠΈΠ½ΡΠ΅ΡΠΏΡΠ΅ΡΠ°Π±ΠΈΠ»Π½Π΅ Π»ΠΎΠ³ΠΈΡΠΊΠ΅ ΠΌΠ΅ΡΠ΅, ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΡΠΊΠ΅ ΠΈ Π½Π΅ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΡΠΊΠ΅, ΠΊΠ°ΠΎ ΠΈ Π΄Π΅ΡΠΊΡΠΈΠΏΡΠΈΠ²Π½ΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΎΡ Π°Π³Π΅Π³Π°ΡΠΈΡΠ΅ β Π»ΠΎΠ³ΠΈΡΠΊΠ° Π°Π³ΡΠ΅Π³Π°ΡΠΈΡΠ°. ΠΠΎΡΠ΅Π΄ ΠΏΡΡΠΆΠ°ΡΠ° ΡΠ΅ΠΎΡΠΈΡΡΠΊΠ΅ ΠΎΡΠ½ΠΎΠ²Π΅, Ρ ΠΎΠ²ΠΎΠΌ ΠΈΡΡΡΠ°ΠΆΠΈΠ²Π°ΡΡ ΠΏΠΎΡΠ΅Π±Π½Π° ΠΏΠ°ΠΆΡΠ° ΡΠ΅ ΠΏΠΎΡΠ²Π΅ΡΠ΅Π½Π° Π΅ΠΌΠΏΠΈΡΠΈΡΡΠΊΠΎΡ Π°Π½Π°Π»ΠΈΠ·ΠΈ. Π£ ΡΠ²ΡΡ
Ρ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΡΠ΅ Π΄Π΅ΡΠΈΠ½ΠΈΡΠ°Π½ΠΈΡ
ΠΌΠ΅ΡΠ° ΡΠ²Π΅Π΄Π΅Π½Π° ΡΠ΅ Π»ΠΎΠ³ΠΈΡΠΊΠ° ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½Π° Π½Π° ΠΠΠ ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ. ΠΠ° ΡΠ²Π΅ ΡΠ²Π΅Π΄Π΅Π½Π΅ ΠΌΠ΅ΡΠ΅ ΠΈΠ·Π²ΡΡΠ΅Π½Π° ΡΠ΅ Π΅Π²Π°Π»ΡΠ°ΡΠΈΡΠ° ΠΈ ΠΏΠΎΡΠ΅ΡΠ΅ΡΠ΅ Π½Π° ΡΠ΅Π°Π»Π½ΠΈΠΌ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈΠ· Π΄ΠΎΠΌΠ΅Π½Π° ΠΌΠ΅Π΄ΠΈΡΠΈΠ½Π΅, Π³Π΄Π΅ ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Π½ΠΎ Π΄Π° ΡΠ²ΠΎΡΠ΅ΡΠ΅ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΠ° ΡΠ½Π°ΠΏΡΠ΅ΡΡΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠ΅ ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΈΡΠ΅. ΠΠ° ΠΊΡΠ°ΡΡ ΡΡ ΠΏΡΠΈΠΊΠ°Π·Π°Π½Π΅ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ Π·Π° ΠΊΠΎΠ½ΡΡΡΡΠΈΡΠ°ΡΠ΅ Π»ΠΎΠ³ΠΈΡΠΊΠΈΡ
ΠΊΠ»Π°ΡΠΈΡΠΈΠΊΠ°ΡΠΎΡΠ° Π·Π°ΡΠ½ΠΎΠ²Π°Π½ΠΈΡ
Π½Π° Π΅ΠΊΡΠΏΠ΅ΡΡΡΠΊΠΈΠΌ ΡΡΠ½ΠΊΡΠΈΡΠ°ΠΌΠ° ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ Π½Π° ΠΏΡΠΎΠ±Π»Π΅ΠΌΡ ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ° Π±Π°Π½ΠΊΡΠΎΡΡΡΠ²Π° ΠΏΡΠ΅Π΄ΡΠ·Π΅ΡΠ°.In this doctoral thesis, a logical approach to similarity modeling based on interpolative Boolean algebra is introduced. Novel interpretable logical measures, both nonparametric and parametrized, are introduced for measuring similarity together with logical aggregation as a descriptive aggregation operator. Besides the theΠΎretical background, in this research special attention is devoted to empirical analysis. For validation purposes, logical classification based on IBA similarity is introduced. Defined logical measures are evaluated and compared in the case of medical data, and it is shown that parameterized measures improve classification results. Finally, the benefits of logic-based classifiers with expert similarity functions are presented on the problem of corporate bankruptcy prediction
No show passengers prediction system based on computational inteligence techniques
Π’Π΅ΠΌΠ° ΠΎΠ²ΠΎΠ³ ΡΠ°Π΄Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΏΡΠ΅Π΄Π»ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° Π·Π° ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ΅ ΠΏΡΡΠ½ΠΈΠΊΠ° ΠΊΠΎΡΠΈ
ΡΠ΅ Π½Π΅ΡΠ΅ ΠΏΠΎΡΠ°Π²ΠΈΡΠΈ Π½Π° Π»Π΅ΡΡ (βno-showβ), ΠΊΠΎΡΠΈ ΡΠ΅ Π·Π°ΡΠ½ΠΈΠ²Π° Π½Π° ΡΠ΅Ρ
Π½ΠΈΠΊΠ°ΠΌΠ° ΡΠ°ΡΡΠ½Π°ΡΡΠΊΠ΅
ΠΈΠ½ΡΠ΅Π»ΠΈΠ³Π΅Π½ΡΠΈΡΠ΅. ΠΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ΅ Π±ΡΠΎΡΠ° βno-showβ ΠΏΡΡΠ½ΠΈΠΊΠ° ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ°Π½ ΠΈ
ΡΡΠΊΠΎ ΡΠΎΡΠΌΡΠ»ΠΈΡΠ°Π½ ΠΏΡΠΎΠ±Π»Π΅ΠΌ ΠΊΠΎΡΠΈ ΡΠ΅ Π²Π΅Ρ Π΄ΡΠΆΠΈ Π½ΠΈΠ· Π³ΠΎΠ΄ΠΈΠ½Π° Π²Π΅ΠΎΠΌΠ° Π°ΠΊΡΡΠ΅Π»Π°Π½ Ρ Π°Π²ΠΈΠΎ
ΠΈΠ½Π΄ΡΡΡΡΠΈΡΠΈ ΠΊΠ°ΠΊΠΎ ΡΠ° ΡΠ΅ΠΎΡΠΈΡΡΠΊΠΎΠ³, ΡΠ°ΠΊΠΎ ΠΈ ΡΠ° ΠΏΡΠ°ΠΊΡΠΈΡΠ½ΠΎΠ³ Π°ΡΠΏΠ΅ΠΊΡΠ°. ΠΠ° ΠΎΡΠ½ΠΎΠ²Ρ
ΠΎΡΠ΅ΠΊΠΈΠ²Π°Π½ΠΎΠ³ Π±ΡΠΎΡΠ° βno showβ ΠΏΡΡΠ½ΠΈΠΊΠ°, ΠΊΠ°ΠΎ ΠΈ Π΄ΡΡΠ³ΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠ°, Π°Π²ΠΈΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡΠ΅ Π΄ΠΎΠ½ΠΎΡΠ΅
ΠΎΠ΄Π»ΡΠΊΡ ΠΎ Π΄ΠΎΠ΄Π°ΡΠ½ΠΎΠΌ Π±ΡΠΎΡΡ ΠΌΠ΅ΡΡΠ° ΠΊΠΎΡΠΈ ΡΠ΅ Π±ΠΈΡΠΈ Π΄ΠΎΡΡΡΠΏΠ°Π½ ΠΊΡΠΎΠ· ΡΠ΅Π·Π΅ΡΠ²Π°ΡΠΈΠΎΠ½ΠΈ ΡΠΈΡΡΠ΅ΠΌ.
ΠΠ° ΠΎΠ²Π°Ρ Π½Π°ΡΠΈΠ½, Π°Π²ΠΈΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡΠ΅ ΠΌΠΎΠ³Ρ ΠΎΡΡΠ²Π°ΡΠΈΡΠΈ Π΄ΠΎΠ΄Π°ΡΠ°Π½ ΠΏΡΠΎΡΠΈΡ, ΠΏΠΎΠ³ΠΎΡΠΎΠ²Ρ ΠΊΠ°Π΄Π° ΡΠ΅
ΡΠ°Π΄ΠΈ ΠΎ Π»Π΅ΡΠΎΠ²ΠΈΠΌΠ° ΠΊΠΎΡΠΈ ΡΡ ΠΏΠΎΠΏΡΡΠ΅Π½ΠΈ Ρ ΠΏΠΎΡΠΏΡΠ½ΠΎΡΡΠΈ.
ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ ΡΠΈΡΡΠ΅ΠΌ Π·Π° ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ΅ βno-showβ ΠΏΡΡΠ½ΠΈΠΊΠ° ΡΠ΅ ΡΠ°ΡΡΠΎΡΠΈ ΠΎΠ΄ Π΄Π²Π΅
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ΅. ΠΡΠ²Π° ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ° ΡΠ΅ ΠΎΠ΄Π½ΠΎΡΠΈ Π½Π° ΠΈΠ·Π±ΠΎΡ Π½Π°ΡΠΏΡΠ΅ΡΠΈΠ·Π½ΠΈΡΠ΅Π³ ΠΌΠΎΠ΄Π΅Π»Π° Π·Π°
ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ΅, Π° Π΄ΡΡΠ³Π° Π½Π° ΠΏΡΠΈΠΌΠ΅Π½Ρ ΠΈ Π²Π°Π»ΠΈΠ΄Π°ΡΠΈΡΡ ΡΠΈΡΡΠ΅ΠΌΠ°. ΠΠΎΠ΄Π΅Π» Π·Π° ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ΅ ΡΠ΅
ΡΠ°ΡΡΠΎΡΠΈ ΠΈΠ· Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΊΠΎΡΠΈ ΡΠ΅ Π·Π°ΡΠ½ΠΈΠ²Π° Π½Π° ΡΠ΅Ρ
Π½ΠΈΡΠΈ Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠ»ΡΡΠ°ΡΠ° ΠΈ
ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»Π°ΡΠΈΠ²Π½Π΅ ΠΡΠ»ΠΎΠ²Π΅ Π°Π»Π³Π΅Π±ΡΠ΅. ΠΠ°ΡΠ΅, ΠΌΠΎΠ΄Π΅Π» ΠΊΠΎΠΌΠ±ΠΈΠ½ΡΡΠ΅ ΠΏΡΠ΅Π΄Π»ΠΎΠ³ ΠΊΠΎΡΠΈ ΡΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ°Π½
ΠΎΠ΄ ΡΡΡΠ°Π½Π΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈ ΠΏΡΠ΅Π΄Π»ΠΎΠ³ ΠΊΠΎΡΠΈ ΠΏΡΠ΅ΠΏΠΎΡΡΡΡΡΠ΅ Π΅ΠΊΡΠΏΠ΅ΡΡ. ΠΠ° ΠΎΠ²Π°Ρ Π½Π°ΡΠΈΠ½
ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ ΡΠΈΡΡΠ΅ΠΌ ΠΎΠ±ΡΠ΅Π΄ΠΈΡΡΡΠ΅ ΠΈ ΡΠ·ΠΈΠΌΠ° Ρ ΠΎΠ±Π·ΠΈΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½Ρ ΠΈ ΡΡΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½Ρ
Π΄ΠΈΠΌΠ΅Π½Π·ΠΈΡΡ ΠΏΡΠΈΠ»ΠΈΠΊΠΎΠΌ ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ°. Π‘Π»ΠΈΡΠ½ΠΎΡΡ ΠΈΠ·ΠΌΠ΅ΡΡ Π»Π΅ΡΠΎΠ²Π° ΡΠ΅ ΠΈΠ·ΡΠ°ΡΡΠ½Π°Π²Π°
ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΈΡ
ΠΌΠ΅ΡΠ° (EΡΠΊΠ»ΠΈΠ΄ΡΠΊΠ° ΠΈ MΠ΅Π½Ρ
Π΅ΡΠ½) ΠΈ ΠΠΠ ΠΌΠ΅ΡΠ΅
ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ. Π’Π°ΠΊΠΎΡΠ΅, ΠΠΠ ΠΏΡΠΈΡΡΡΠΏ ΡΠΏΠΎΡΠΏΡΡΡΡΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΈ Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌ ΡΠ΅Ρ
Π½ΠΈΠΊΠ΅
Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠ»ΡΡΠ°ΡΠ° ΠΊΡΠΎΠ· ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π°ΡΠ΅ Π»ΠΎΠ³ΠΈΡΠΊΠ΅ Π°Π³ΡΠ΅Π³Π°ΡΠΈΡΠ΅ Π²ΡΠ΅Π΄Π½ΠΎΡΡΠΈ,
ΠΎΠ΄Π½ΠΎΡΠ½ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΎΠ²Π°ΡΠ΅ΠΌ ΠΏΠΎΡΡΠΎΡΠ΅ΡΠΈΡ
Π½Π΅Π»ΠΈΠ½Π΅Π°ΡΠ½ΠΈΡ
Π·Π°Π²ΠΈΡΠ½ΠΎΡΡΠΈ ΠΈΠ·ΠΌΠ΅ΡΡ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°.
ΠΡΠΈΠΌΠ΅Π½Π° ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎΠ³ ΡΠΈΡΡΠ΅ΠΌΠ° ΡΠ΅ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ΅Π½Π° ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ° ΠΎ
Π»Π΅ΡΡ Π½Π° ΡΠ΅Π»Π°ΡΠΈΡΠΈ ΠΠ΅ΠΎΠ³ΡΠ°Π΄ - ΠΠΌΡΡΠ΅ΡΠ΄Π°ΠΌ, Π·Π° ΠΏΠ΅ΡΠΈΠΎΠ΄ ΠΎΠ΄ Π³ΠΎΠ΄ΠΈΠ½Ρ Π΄Π°Π½Π°. ΠΠΎΠ±ΠΈΡΠ΅Π½ΠΈ
ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΠΏΠΎΠΊΠ°Π·ΡΡΡ Π΄Π° ΡΠ΅ Π½Π΅ΠΎΠΏΡ
ΠΎΠ΄Π½ΠΎ ΡΠΊΡΡΡΠΈΡΠΈ ΠΏΡΠ΅ΠΏΠΎΡΡΠΊΡ Π΅ΠΊΡΠΏΠ΅ΡΡΠ° Ρ ΠΏΡΠΎΡΠ΅Ρ
ΠΏΡΠ΅Π΄Π²ΠΈΡΠ°ΡΠ°, ΠΊΠ°ΠΎ ΠΈ Π΄Π° ΡΠ°ΠΌ Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌ Π½ΠΈΡΠ΅ Π΄ΠΎΠ²ΠΎΡΠ°Π½ Π΄Π° Π±ΠΈ ΡΠ΅ Π΄ΠΎΠ±ΠΈΠ»ΠΈ Π΄ΠΎΠ²ΠΎΡΠ½ΠΎ
ΠΏΡΠ΅ΡΠΈΠ·Π½ΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ. Π’Π°ΠΊΠΎΡΠ΅, Π΄ΠΎΠ±ΠΈΡΠ΅Π½ΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΡΠΊΠ°Π·ΡΡΡ Π΄Π° ΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΊΠΎΡΠΈ ΡΡ
Π·Π°ΡΠ½ΠΎΠ²Π°Π½ΠΈ Π½Π° ΠΠΠ ΠΏΡΠΈΡΡΡΠΏΡ ΠΈ ΠΊΠΎΡΠΈ ΠΊΠΎΠΌΠ±ΠΈΠ½ΡΡΡ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠ΅ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° ΠΈ ΠΏΡΠ΅ΠΏΠΎΡΡΠΊΡ
Π΅ΠΊΡΠΏΠ΅ΡΡΠ°, ΠΏΡΠ΅ΡΠΈΠ·Π½ΠΈΡΠΈ ΠΎΠ΄ ΠΌΠΎΠ΄Π΅Π»Π° ΠΊΠΎΡΠΈ ΠΊΠΎΡΠΈΡΡΠ΅ ΡΡΠ°Π΄ΠΈΡΠΈΠΎΠ½Π°Π»Π½Π΅ ΠΌΠ΅ΡΠ΅ Π·Π°
ΠΈΠ·ΡΠ°ΡΡΠ½Π°Π²Π°ΡΠ΅ ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ. Π‘Ρ
ΠΎΠ΄Π½ΠΎ ΡΠΎΠΌΠ΅, ΠΏΠΎΡΠ²ΡΡΠ΅Π½ΠΎ ΡΠ΅ Π΄Π° Π»ΠΎΠ³ΠΈΡΠΊΠΈ ΠΏΡΠΈΡΡΡΠΏ
ΠΌΠΎΠ΄Π΅Π»ΠΎΠ²Π°ΡΡ ΡΠ»ΠΈΡΠ½ΠΎΡΡΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΏΠ΅ΡΡΠΏΠ΅ΠΊΡΠΈΠ²Π°Π½ ΠΏΡΠ°Π²Π°Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅ Ρ ΠΎΠΊΠ²ΠΈΡΡviii
ΡΠ΅Ρ
Π½ΠΈΠΊΠ΅ Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ° Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠ»ΡΡΠ°ΡΠ°. Π‘Π° ΠΏΡΠ°ΠΊΡΠΈΡΠ½Π΅ ΡΡΡΠ°Π½Π΅, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΎ
ΡΠ΅ΡΠ΅ΡΠ΅ ΡΠ΅ ΡΠ΅Π΄Π½ΠΎΡΡΠ°Π²Π½ΠΎ Π·Π° ΡΠ°Π·ΡΠΌΠ΅Π²Π°ΡΠ΅ Ρ ΠΏΠΎΠ³Π»Π΅Π΄Ρ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΠ°ΡΠ°, ΠΈ ΠΌΠΎΠΆΠ΅ ΡΠ΅ Π΄ΠΎΡΡΠ°
ΡΠ΅Π΄Π½ΠΎΡΡΠ°Π²Π½ΠΎ ΠΈΠΌΠΏΠ»Π΅ΠΌΠ΅Π½ΡΠΈΡΠ°ΡΠΈ ΠΈ ΠΏΡΠΈΠ»Π°Π³ΠΎΠ΄ΠΈΡΠΈ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ½ΠΎΡΡΠΈΠΌΠ° ΠΈ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΡΠ°ΠΌΠ°
Π°Π²ΠΈΠΎ ΠΊΠΎΠΌΠΏΠ°Π½ΠΈΡΠ΅.In this doctorial dissertation no-show passengers prediction system based on
computational intelligence techniques is proposed. Predicting no-show passengers
represents a specific and concisely formulated problem that actively persists for a longer
period of time in the airline industry from both theoretical and practical perspective.
Based on the expected number of no-show passengers, as well as some other factors,
airlines are making decisions about how many additional seats will be allowed for
overbooking through reservation system. This way, airlines could make additional
profit, especially when it comes to the high demanding flights that are fully booked.
Proposed prediction system for no-show passengers consists of two major
components. First component considers selecting the best performing prediction model
from the available model pool, and the second component is related to the model
validation and application. Prediction model is based on the algorithm that combines
case based reasoning technique and interpolative Boolean algebra (IBA) approach.
Furthermore, model combines prediction recommendation generated by algorithm and
recommendation provided from the domain expert. This way, the proposed system
considers and takes into account both objective and subjective dimension. Similarity
between flights is determined using traditional metrics (Euclidean and Manhattan) and
IBA similarity measure. Also, IBA approach is enhancing the conventional CBR
algorithm by enabling logical aggregation of values, i.e. capturing existing nonlinear
dependencies in the data.
The usage of the proposed system is illustrated in the numerical example
regarding a single leg flight on the Belgrade-Amsterdam route and covers a one-year
period. The obtained results show the necessity to include expert knowledge in
prediction process, i.e. the CBR algorithm used alone is insufficient to produce results
that are accurate enough. Furthermore, the results are indicating that the IBA-based
models that combine the results of the CBR algorithm and expert recommendations
perform better than distance-based models. Therefore, it is confirmed that the logicbased approach of similarity modelling is the prospective direction within the CBR
algorithm. From a practical side, proposed solution is easy for understanding from thex
functional aspect, and could be easily implemented and adjusted according to airline
operations
Concepts modelling using consistent fuzzy logic
ΠΠΎΠ½ΡΠ΅ΠΏΡΠΈ ΡΡ ΠΌΠ΅Π½ΡΠ°Π»Π½Π΅ ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΡΠ΅ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠ°. Π’Π΅ΠΎΡΠΈΡΠ° ΠΊΠΎΠ½ΡΠ΅ΠΏΠ°ΡΠ° ΡΡΠ΅Π±Π° Π΄Π° ΠΎΠ±ΡΠ°ΡΠ½ΠΈ ΠΊΠ°ΠΊΠΎ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈ ΡΡΠ½ΠΊΡΠΈΠΎΠ½ΠΈΡΡ ΠΊΠ°ΠΎ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠ΅ ΠΈ ΠΊΠ°ΠΊΠΎ ΡΠ΅ ΠΊΠΎΠΌΠΏΠΎΠ½ΡΡΡ Ρ ΡΠ»ΠΎΠΆΠ΅Π½Π΅ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠ΅ ΠΈ ΠΌΠΈΡΠ»ΠΈ. ΠΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΎΡΡ ΠΎΠ±ΡΠ°ΡΡΠ°Π²Π° ΠΊΡΠ΅Π°ΡΠΈΠ²Π½ΠΎΡΡ, ΠΊΠ°ΠΊΠΎ ΠΊΠΎΠ½Π°ΡΠ½ΠΈ ΡΠΌ ΠΌΠΎΠΆΠ΅ ΠΈΠΌΠ°ΡΠΈ Π±Π΅ΡΠΊΠΎΠ½Π°ΡΠ°Π½ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½ΠΈ ΠΊΠ°ΠΏΠ°ΡΠΈΡΠ΅Ρ. ΠΡΡΡΠ½ΠΎ ΠΏΠΈΡΠ°ΡΠ΅ Ρ ΡΠ΅ΠΎΡΠΈΡΠΈ ΡΠ΅ Π΄Π° Π»ΠΈ ΡΠ΅ ΡΡΠ΅ΠΏΠ΅Π½ ΠΏΡΠΈΠΌΠ΅Π½Π΅ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠ° Π±ΠΈΠ½Π°ΡΠ°Π½ ΠΈΠ»ΠΈ Π³ΡΠ°Π΄ΠΈΡΠ°Π½. Π’Π΅ΠΎΡΠΈΡΠ° Π³ΡΠ°Π΄Π°ΡΠΈΡΠ΅ Π΄ΠΎΠ΄Π°ΡΠ½ΠΎ ΠΎΠ±ΡΠ°ΡΡΠ°Π²Π° ΡΠ΅ΠΏΡΠ΅Π·Π΅Π½ΡΠ°ΡΠΈΠ²Π½ΠΎΡΡ ΠΈΠ½ΡΡΠ°Π½ΡΠΈ ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠ°, Π°Π»ΠΈ ΡΠ΅ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ°ΡΠΈ ΠΊΠ»Π°ΡΠΈΡΠ½ΠΎΠ³ ΠΏΠΎΠ³Π»Π΅Π΄Π° ΠΊΡΠΈΡΠΈΠΊΡΡΡ Π΄Π° Π½Π΅ ΠΌΠΎΠΆΠ΅ ΠΎΠ±ΡΠ°ΡΠ½ΠΈΡΠΈ ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΎΡΡ ΡΠ΅ Π·Π°ΠΊΡΡΡΡΡΡ Π΄Π° ΡΠ΅ ΠΏΠΎΠ³ΡΠ΅ΡΠ½Π°.
Π£ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠΈ ΡΡ ΠΏΠΎΠΌΠΎΡΡ ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½Π΅ ΡΠ°Π·ΠΈ Π»ΠΎΠ³ΠΈΠΊΠ΅ ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½ΠΈ ΠΈ ΠΎΠ±ΡΠ°ΡΡΠ΅Π½ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈ Ρ ΡΠ»ΡΡΠ°ΡΡ Π³ΡΠ°Π΄Π°ΡΠΈΡΠ΅. ΠΡΠΎΡΠΎΡΠΈΠΏ ΡΠ΅ΠΎΡΠΈΡΠ° ΠΊΠΎΠ½ΡΠ΅ΠΏΠ°ΡΠ° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π° ΡΠ΅ Ρ ΡΡΠ°Π½Π΄Π°ΡΠ΄Π½ΠΎΠΌ ΠΈ ΡΠ»ΡΡΠ°ΡΡ Π»ΠΎΠ³ΠΈΡΠΊΠΈΡ
ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΡΠ° ΠΈΠ·ΠΌΠ΅ΡΡ Π°ΡΡΠΈΠ±ΡΡΠ°. Π’Π΅ΠΎΡΠΈΡΠ° Π΅Π³Π·Π΅ΠΌΠΏΠ»Π°ΡΠ° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π° ΡΠ΅ Π½Π° Π΄Π²Π° Π½Π°ΡΠΈΠ½Π°. Π’Π΅ΠΎΡΠΈΡΠ° Π³ΡΠ°Π½ΠΈΡΠ° ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠ° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π° ΡΠ΅ Ρ Π±ΠΈΠΏΠΎΠ»Π°ΡΠ½ΠΎΠΌ ΠΎΠΊΠ²ΠΈΡΡ. ΠΠ°ΡΠ·Π°Π»Π½Π° ΡΠ΅ΠΎΡΠΈΡΠ° ΠΊΠΎΠ½ΡΠ΅ΠΏΠ°ΡΠ° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π° ΡΠ΅ Ρ ΡΡΠ°Π½Π΄Π°ΡΠ΄Π½ΠΎΠΌ, ΡΠ»ΡΡΠ°ΡΡ ΠΊΠ°ΡΠ·Π°Π»Π½ΠΈΡ
ΠΏΠΎΠ²ΡΠ°ΡΠ½ΠΈΡ
ΡΠΏΡΠ΅Π³Π° ΠΈΠ·ΠΌΠ΅ΡΡ Π°ΡΡΠΈΠ±ΡΡΠ° ΠΈ/ ΠΈΠ»ΠΈ Π»ΠΎΠ³ΠΈΡΠΊΠΈΡ
ΠΈΠ½ΡΠ΅ΡΠ°ΠΊΡΠΈΡΠ° ΠΈΠ·ΠΌΠ΅ΡΡ ΡΠ·ΡΠΎΠΊΠ°. ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠΊΠΈ ΡΠ΅ ΠΎΠ±ΡΠ°ΡΡΠ΅Π½Π° ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡΠΈΠΎΠ½Π°Π»Π½ΠΎΡΡ ΠΡΠ»ΠΎΠ²ΠΈΡ
ΠΊΠΎΠ½ΡΠ΅ΠΏΠ°ΡΠ° Ρ ΡΠΊΠ»Π°Π΄Ρ ΡΠ° ΠΏΡΠΎΡΠΎΡΠΈΠΏ, Π΅Π³Π·Π΅ΠΌΠΏΠ»Π°Ρ ΠΈ ΡΠ΅ΠΎΡΠΈΡΠΎΠΌ Π³ΡΠ°Π½ΠΈΡΠ° ΠΊΠ°ΡΠ΅Π³ΠΎΡΠΈΡΠ°, ΠΏΠΎΠ΄ ΠΏΡΠ΅ΡΠΏΠΎΡΡΠ°Π²ΠΊΠΎΠΌ Π΄Π° ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈ ΠΈΠΌΠ°ΡΡ Π²Π΅ΠΊΡΠΎΡΡΠΊΡ ΠΏΡΠΈΡΠΎΠ΄Ρ. ΠΠ΄Π³ΠΎΠ²ΠΎΡΠ΅Π½ΠΎ ΡΠ΅ Π½Π° ΠΊΡΠΈΡΠΈΠΊΡ ΠΏΡΠΎΡΠΎΡΠΈΠΏ ΡΠ΅ΠΎΡΠΈΡΠ΅ ΠΈ ΠΎΠΏΠΎΠ²ΡΠ³Π½ΡΡΠ° ΡΡ Π΄Π²Π° Π°ΡΠ³ΡΠΌΠ΅Π½ΡΠ° ΠΊΠΎΡΠΈΠΌΠ° ΡΠ΅ ΠΎΠ½Π° ΠΏΠΎΡΠΊΡΠ΅ΠΏΡΡΡΠ΅, Π΄Π° Π·Π°ΠΊΠΎΠ½ΠΈ ΠΈΡΠΊΡΡΡΠ΅ΡΠ° ΡΡΠ΅ΡΠ΅Π³ ΠΈ Π½Π΅ΠΊΠΎΠ½ΡΡΠ°Π΄ΠΈΠΊΡΠΈΡΠ΅ Π½ΠΈΡΡ Π·Π°Π³Π°ΡΠ°Π½ΡΠΎΠ²Π°Π½ΠΈ Ρ ΠΏΡΠΎΡΠΎΡΠΈΠΏ ΠΎΠΊΠ²ΠΈΡΡ ΠΈ Π΄Π° Π½Π΅ΠΎΠ΄ΡΠ΅ΡΠ΅Π½ΠΎ ΠΌΠ½ΠΎΠ³ΠΎ ΠΡΠ»ΠΎΠ²ΠΈΡ
ΠΊΠΎΠ½ΡΠ΅ΠΏΠ°ΡΠ° Π½Π΅ΠΌΠ° ΠΏΡΠΎΡΠΎΡΠΈΠΏ ΠΈΠ°ΠΊΠΎ Π³Π° ΡΠ²Π΅ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠ΅ ΠΈΠΌΠ°ΡΡ. ΠΠΎΠ΄Π΅Π»ΠΎΠ²Π°Π½ΠΈ ΡΡ ΠΊΠΎΠ½ΠΊΡΠ΅ΡΠ½ΠΈ ΠΡΠ»ΠΎΠ²ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈ Π·Π° ΠΊΠΎΡΠ΅ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ°ΡΠΈ ΠΊΠ»Π°ΡΠΈΡΠ½ΠΎΠ³ ΠΏΠΎΠ³Π»Π΅Π΄Π° ΡΠΌΠ°ΡΡΠ°ΡΡ Π΄Π° Π½Π΅ ΠΌΠΎΠ³Ρ Π±ΠΈΡΠΈ ΠΎΠ±ΡΠ°ΡΡΠ΅Π½ΠΈ Ρ ΠΏΡΠΎΡΠΎΡΠΈΠΏ ΠΎΠΊΠ²ΠΈΡΡ.
Π£ Π½Π°ΡΡΠ°Π²ΠΊΡ Π΄ΠΈΡΠ΅ΡΡΠ°ΡΠΈΡΠ΅ Π³Π΅Π½Π΅ΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½Π° ΡΡ Π΄Π²Π° Π΅ΠΊΡΠΏΠ΅ΡΡΡΠΊΠ° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·ΠΌΠ° Π·Π° ΡΠ»ΠΎΠΆΠ΅Π½e ΡΠΈΡΡΠ΅ΠΌΠ΅, ΠΡΠ»ΠΎΠ²Π΅ ΠΌΡΠ΅ΠΆΠ΅ ΠΈ ΡΠ°Π·ΠΈ ΠΊΠΎΠ³Π½ΠΈΡΠΈΠ²Π½Π΅ ΠΌΠ°ΠΏΠ΅. ΠΠ΅ΡΠΊΡΠΈΠΏΡΠΈΠ²Π½Π° ΡΠ½Π°Π³Π° ΡΠΎΡΠΌΠ°Π»ΠΈΠ·Π°ΠΌΠ° Π΄ΡΠ°ΡΡΠΈΡΠ½ΠΎ ΡΠ΅ ΡΠ²Π΅ΡΠ°Π½Π° ΡΠΈΠΌΠ΅ ΠΎΠ½ΠΈ ΠΏΠΎΡΡΠ°ΡΡ ΡΠΏΠΎΡΡΠ΅Π±ΡΠΈΠ²ΠΈ Ρ Π·Π½Π°ΡΠ½ΠΎ ΡΠΈΡΠ΅ΠΌ ΡΠΏΠ΅ΠΊΡΡΡ ΡΠΈΡΡΠ°ΡΠΈΡΠ°...Concepts are mental representations of categories. Theory of concepts has to explain how concepts function as categories and how they compose into complex concepts and thoughts. The compositionality explains creativity β how a finite mind can have an infinite cognitive capacity. Fundamental question in theory of concepts is whether a degree of conceptual application is binary or graded. Theory of gradation additionally explains representativeness of category members, but it is criticized by classical theoreticians that it cannot explain compositionality, and thus, it is considered wrong.
In this doctoral dissertation, concepts in the case of gradation are formalized and explained using consistent fuzzy logic. Prototype theory of concepts is formalized in standard case and in case of logical interactions between attributes. Exemplar theory of concepts is formalized in two ways. Boundary theory of concepts is formalized in bipolar framework. Causal theory of concepts is formalized in standard case, in case of causal feedback between attributes and/ or logical interactions between causes. The compositionality of Boolean concepts is mathematically explained according to the prototype, exemplar and boundary theory, under the assumption that concepts have vector nature. The critique of the prototype theory has been addressed, and two arguments that support the critique are refuted - that laws of excluded middle and noncontradiction are not secured in the prototype framework, and that there isnβt any prototype for indefinitely many Boolean concepts even though all components have one. Specific Boolean concepts, which are considered unexplainable in the prototype framework by classical theoreticians, are modelled.
In the rest of the dissertation, two expert-based formalisms for complex systems, Boolean networks and fuzzy cognitive maps, are generalized. The descriptive power of two formalisms is drastically increased, which allows their usage in much broader spectrum of cases..
NASA Space Engineering Research Center for VLSI System Design
This annual report outlines the activities of the past year at the NASA SERC on VLSI Design. Highlights for this year include the following: a significant breakthrough was achieved in utilizing commercial IC foundries for producing flight electronics; the first two flight qualified chips were designed, fabricated, and tested and are now being delivered into NASA flight systems; and a new technology transfer mechanism has been established to transfer VLSI advances into NASA and commercial systems
A consistent neuro-fuzzy inference system
ΠΠ΅Π»ΠΈΠΊΠΈ Π±ΡΠΎΡ Π°ΡΡΠΎΡΠ° ΡΠΌΠ°ΡΡΠ° Π΄Π° Π²Π΅Π»ΠΈΠΊΠ΅ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡΠΈ Π΅ΠΊΡΠΏΠ΅ΡΡΡΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌΠ° Π»Π΅ΠΆΠ΅ Ρ Ρ
ΠΈΠ±ΡΠΈΠ΄Π½ΠΈΠΌ ΠΌΠΎΠ΄Π΅Π»ΠΈΠΌΠ°, ΡΡΠΎ ΡΡ ΠΎΠ²ΠΈ ΡΠΈΡΡΠ΅ΠΌΠΈ ΠΈ Π΄ΠΎΠΊΠ°Π·Π°Π»ΠΈ Ρ ΠΏΡΠ°ΠΊΡΠΈ. ΠΠΎΡΠΈΠ²ΠΈΡΠ°Π½ ΡΠΈΠΌΠ΅, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ ΠΌΠΎΠ΄Π΅Π» ΡΠΈΡΡΠ΅ΠΌΠ° Ρ ΠΎΡΠ½ΠΎΠ²ΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²ΡΠ° ΠΈΠ½ΡΠ΅Π³ΡΠ°ΡΠΈΡΡ Π½Π΅ΡΡΠΎΠ½ΡΠΊΠΈΡ
ΠΌΡΠ΅ΠΆΠ° ΠΈ ΡΠ°Π·ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ°, ΡΠΈΠΌΠ΅ ΡΠ΅ Π±ΠΎΡΠ΅ ΠΊΠΎΡΠΈΡΡΠ΅ Π΄ΠΎΠ±ΡΠ΅ ΡΡΡΠ°Π½Π΅ ΠΎΠ±Π° ΠΏΡΠΈΡΡΡΠΏΠ°.
ΠΠΎΠ»Π°Π·Π½Π° ΠΎΡΠ½ΠΎΠ²Π° ΠΎΠ²ΠΎΠ³ ΡΠ°Π΄Π° ΡΠ΅ Π΄Π° ΠΏΠΎΠ½Π°ΡΠ°ΡΠ΅ ΡΠΈΡΡΠ΅ΠΌΠ°, ΠΊΡΠΎΠ· ΡΠΊΡΠΏ Π»ΠΈΠ½Π³Π²ΠΈΡΡΠΈΡΠΊΠΈΡ
ΠΏΡΠ°Π²ΠΈΠ»Π°, ΡΡΠ΅Π±Π° Π΄Π° ΠΎΠΏΠΈΡΡΡΡ ΡΠΏΡΠ°Π²ΠΎ ΠΎΠ½ΠΈ ΠΊΠΎΡΠΈ ΡΠΈΡΡΠ΅ΠΌ Π½Π°ΡΠ²ΠΈΡΠ΅ ΠΏΠΎΠ·Π½Π°ΡΡ ΠΈ ΡΠ°Π·ΡΠΌΠ΅ΡΡ (Π½Π°ΡΡΠΏΡΠΎΡ Π°ΡΡΠΎΠΌΠ°ΡΡΠΊΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ°Π½ΠΈΠΌ ΠΏΡΠ°Π²ΠΈΠ»ΠΈΠΌΠ° ΠΊΠΎΡΠ° ΡΡ Π½Π°ΡΡΠ΅ΡΡΠ΅ ΡΠΎΠ³ΠΎΠ±Π°ΡΠ½Π° ΠΈ Π½Π΅ΡΠ°Π·ΡΠΌΡΠΈΠ²Π°). ΠΠ½Π°ΡΠ΅ Π΅ΠΊΡΠΏΠ΅ΡΠ°ΡΠ° ΠΈΠ· Π±ΠΈΠ»ΠΎ ΠΊΠΎΡΠ΅ ΠΎΠ±Π»Π°ΡΡΠΈ Π»Π°ΠΊΠΎ ΡΠ΅ ΠΌΠΎΠΆΠ΅ ΡΠΎΡΠΌΡΠ»ΠΈΡΠ°ΡΠΈ Π²Π΅ΡΠ±Π°Π»Π½ΠΈΠΌ ΠΈΡΠΊΠ°Π·ΠΈΠΌΠ°, Π° ΡΠ΅ΠΎΡΠΈΡΠ° ΡΠ°Π·ΠΈ ΡΠΊΡΠΏΠΎΠ²Π° ΠΈ ΡΠ°Π·ΠΈ Π»ΠΎΠ³ΠΈΠΊΠ΅ ΠΎΠΌΠΎΠ³ΡΡΠ°Π²Π° ΠΏΡΠ΅Π²ΠΎΡΠ΅ΡΠ΅ ΠΎΠ²Π°ΠΊΠ²ΠΈΡ
ΠΈΡΠΊΠ°Π·Π° Ρ ΠΎΠ΄Π³ΠΎΠ²Π°ΡaΡΡΡΠ΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠΊΠ΅ ΠΈΠ·ΡΠ°Π·Π΅.
ΠΠ»Π°ΡΠΈΡΠ½Π° ΡΠ΅ΠΎΡΠΈΡΠ° ΡΠ°Π·ΠΈ ΡΠΊΡΠΏΠΎΠ²Π° Π½Π΅ Π·Π°Π΄ΠΎΠ²ΠΎΡΠ°Π²Π° ΡΠ²Π΅ ΠΡΠ»ΠΎΠ²Π΅ Π°ΠΊΡΠΈΠΎΠΌΠ΅. ΠΠ· ΠΎΠ²ΠΎΠ³ ΡΠ°Π·Π»ΠΎΠ³Π° Ρ ΡΠ°Π΄Ρ ΡΠ΅ ΠΏΡΠΈΠΌΠ΅ΡΠ΅Π½Π° ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½Π° ΡΠ΅Π°Π»Π½ΠΎ-Π²ΡΠ΅Π΄Π½ΠΎΡΠ½Π° [0,1] Π»ΠΎΠ³ΠΈΠΊΠ°, ΠΊΠΎΡΠ° ΡΠ΅ Π·Π°ΡΠ½ΠΈΠ²Π° Π½Π° ΠΈΠ½ΡΠ΅ΡΠΏΠΎΠ»Π°ΡΠΈΠ²Π½ΠΎΡ ΠΡΠ»ΠΎΠ²ΠΎΡ Π°Π»Π³Π΅Π±ΡΠΈ (ΠΠΠ). Π‘Π²Π°ΠΊΠ° Π»ΠΎΠ³ΠΈΡΠΊΠ° ΡΡΠ½ΠΊΡΠΈΡΠ° ΠΌΠΎΠΆΠ΅ ΡΠ΅ ΡΠ΅Π΄Π½ΠΎΠ·Π½Π°ΡΠ½ΠΎ ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΠ°ΡΠΈ Ρ ΠΎΠ΄Π³ΠΎΠ²Π°ΡΠ°ΡΡΡΠΈ Π³Π΅Π½Π΅ΡΠ°Π»ΠΈΠ·ΠΎΠ²Π°Π½ΠΈ ΠΡΠ»ΠΎΠ² ΠΏΠΎΠ»ΠΈΠ½ΠΎΠΌ (ΠΠΠ) ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ΅ΠΌ ΠΠΠ ΠΏΡΠΈ ΡΠ΅ΠΌΡ ΡΠ΅ ΡΡΠ²Π°ΡΡ ΡΠ²ΠΈ ΠΡΠ»ΠΎΠ²ΠΈ Π·Π°ΠΊΠΎΠ½ΠΈ.
ΠΠΏΡΠ°Π²Π΄Π°Π½ΠΎΡΡ ΠΊΠΎΡΠΈΡΡΠ΅ΡΠ° ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΠ³ ΠΏΡΠΈΡΡΡΠΏΠ° Π½Π°ΡΠΏΡΠ΅ ΡΠ΅ ΠΈΠ»ΡΡΡΡΠΎΠ²Π°Π½Π° Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ½ΠΎΠ³ ΡΠ°Π·ΠΈ ΡΠΈΡΡΠ΅ΠΌΠ° Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ° (ΠΠ€ΠΠ‘). Π‘Π²ΡΡ
Π° ΠΏΡΠΈΠΊΠ°Π·Π°Π½ΠΎΠ³ ΠΠ€ΠΠ‘-Π° ΡΠ΅ Π΄Π° ΠΏΡΠΎΡΠ΅Π½ΠΈ ΠΌΠΎΠ³ΡΡΠ½ΠΎΡΡ Π΄Π° ΡΠ΅ ΠΏΠ°ΡΠΈΡΠ΅Π½Ρ Π½Π° Π΄ΠΈΡΠ°Π»ΠΈΠ·ΠΈ ΡΡΠ±ΡΡΠ½Π΅ ΠΌΠ°ΡΠ°ΠΌΠΈΡΠ΅ (Π»Π°Ρ. peritoneum) ΠΎΠ±ΠΎΠ»Π΅ΠΎ ΠΎΠ΄ ΠΏΠ΅ΡΠΈΡΠΎΠ½ΠΈΡΠΈΡΠ°. ΠΠΎΠ±ΠΈΡΠ΅Π½ΠΈ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈ ΡΠΊΠ°Π·ΡΡΡ Π½Π° ΡΠΈΡΠ΅Π½ΠΈΡΡ Π΄Π° ΠΊΠ»Π°ΡΠΈΡΠ°Π½ Π€ΠΠ‘ ΠΈ ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ°Π½ ΠΏΡΠΈΡΡΡΠΏ Π½Π΅ Π²ΠΎΠ΄Π΅ ΡΠ²Π΅ΠΊ ΠΊΠ° ΠΈΡΡΠΈΠΌ ΡΠ΅Π·ΡΠ»ΡΠ°ΡΠΈΠΌΠ°, Π° ΡΠ°Π·Π»ΠΈΠΊΠ° ΡΠ΅ Π½Π°ΡΡΠΎΡΡΠΈΠ²ΠΈΡΠ° ΠΊΠ°Π΄Π° ΠΏΡΠ°Π²ΠΈΠ»Π° ΡΠΊΡΡΡΡΡΡ Π½Π΅Π³Π°ΡΠΈΡΡ.
ΠΠ°ΠΊΠΎ Π±ΠΈ ΡΠ΅ ΠΠ€ΠΠ‘ Π΄Π°ΡΠ΅ ΡΠ½Π°ΠΏΡΠ΅Π΄ΠΈΠΎ, ΠΊΠΎΡΠΈΡΡΠ΅Π½Π° ΡΠ΅ Π½Π΅ΡΡΠΎΠ½ΡΠΊΠ° ΠΌΡΠ΅ΠΆΠ°, ΡΡ. ΡΠ΅Π½ Π°Π»Π³ΠΎΡΠΈΡΠ°ΠΌ ΡΡΠ΅ΡΠ°, ΠΊΠΎΡΠΈ, Π½Π° ΠΎΡΠ½ΠΎΠ²Ρ ΡΠΊΡΠΏΠ° ΡΠ»Π°Π·Π½ΠΎ-ΠΈΠ·Π»Π°Π·Π½ΠΈΡ
ΠΏΠΎΠ΄Π°ΡΠ°ΠΊΠ°, ΠΏΠΎΠ΄Π΅ΡΠ°Π²Π° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ΅ ΡΠ°ΠΊΠΎ Π΄Π° Π²ΠΈΡΠ΅ ΠΎΠ΄Π³ΠΎΠ²Π°ΡΠ°ΡΡ ΡΠ΅Π°Π»Π½ΠΎΠΌ ΡΠΈΡΡΠ΅ΠΌΡ. ΠΠ° ΡΠ°Ρ Π½Π°ΡΠΈΠ½, ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½ΠΈ
ΠΊΠΎΠ½Π·ΠΈΡΡΠ΅Π½ΡΠ°Π½ Π½Π΅ΡΡΠΎ-ΡΠ°Π·ΠΈ ΡΠΈΡΡΠ΅ΠΌ (ΠΠΠ€ΠΠ‘) ΠΊΠΎΡΠΈΡΡΠΈ Π·Π½Π°ΡΠ΅ ΡΠ°Π΄ΡΠΆΠ°Π½ΠΎ Ρ ΠΏΠΎΠ΄Π°ΡΠΈΠΌΠ° ΠΈ ΡΠ½Π°ΠΏΡΠ΅ΡΡΡΠ΅ Π·Π°ΠΊΡΡΡΠΈΠ²Π°ΡΠ΅. Π’Π°ΠΊΠΎΡΠ΅, Π΅Π»ΠΈΠΌΠΈΠ½ΠΈΡΠ΅ ΡΠ΅ ΡΡΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΡΡ ΠΊΠΎΡΡ Π΅ΠΊΡΠΏΠ΅ΡΡΠΈ Ρ Π½Π΅ΠΊΠΎΡ ΠΌΠ΅ΡΠΈ ΠΈΠ·ΡΠ°ΠΆΠ°Π²Π°ΡΡ ΠΏΡΠΈΠ»ΠΈΠΊΠΎΠΌ Π΄Π΅ΡΠΈΠ½ΠΈΡΠ°ΡΠ° ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΠ°ΡΠ° ΡΠΈΡΡΠ΅ΠΌΠ°...A number of authors find that the greatest potential of expert systems lies in hybrid models, and such models have proven this viewpoint in practice.Therein lies the motivation for introducing a new system model, integrating neural networks and fuzzy systems, thus building on the best features of each of these approaches.
The main premise of this thesis is that the behavior of a system should be described, through a set of linguistic rules, by those who know and understand the system the best (as opposed to the automatic generation of rules that are often cumbersome and incomprehensible). Expert knowledge in any domain can be easily expressed in the form of verbal statements, and fuzzy set theory and fuzzy logic enable the transformation of such verbal statements into mathematical expressions.
Conventional fuzzy set theory does not satisfy all Boolean axioms. For this reason, the consistent real-valued [0,1] logic, based on the Interpolative realization of Boolean algebra (IBA), is applied in this thesis. Any logical function can be uniquely transformed into a corresponding generalized Boolean polynomial (GBP) using IBA thereby preserving all Boolean laws.
The justification for using a consistent approach is first illustrated on an example of a consistent fuzzy inference system (CFIS). The purpose of the described CFIS is to estimate the likelihood that a patient undergoing peritoneal dialysis, has peritonitis. The obtained results demonstrate that conventional FIS and the Boolean consistent approach do not always lead to the same results, and this discrepancy is most pronounced when the established rules include negations.
In order to further enhance CFIS a neural network, or, more precisely, its learning algorithm, is used to fine-tune the parameters, in accordance with a set of input-output data, so that the parameters better suit the real system. Consequently, the proposed
consistent neuro-fuzzy system (CNFIS) uses the knowledge contained in the data to improve the inference process. In addition, it eliminates the subjectivity incorporated into the system by experts when defining the parameters of the system..
Computer-Aided Geometry Modeling
Techniques in computer-aided geometry modeling and their application are addressed. Mathematical modeling, solid geometry models, management of geometric data, development of geometry standards, and interactive and graphic procedures are discussed. The applications include aeronautical and aerospace structures design, fluid flow modeling, and gas turbine design
Connected Attribute Filtering Based on Contour Smoothness
A new attribute measuring the contour smoothness of 2-D objects is presented in the context of morphological attribute filtering. The attribute is based on the ratio of the circularity and non-compactness, and has a maximum of 1 for a perfect circle. It decreases as the object boundary becomes irregular. Computation on hierarchical image representation structures relies on five auxiliary data members and is rapid. Contour smoothness is a suitable descriptor for detecting and discriminating man-made structures from other image features. An example is demonstrated on a very-high-resolution satellite image using connected pattern spectra and the switchboard platform