Fuzzy system based on two-step cascade genetic optimization strategy for tobacco tar prediction

  • There are many challenges in accurately measuring cigarette tar constituents. These include the need for standardized smoke generation methods related to unstable mixtures. In this research were developed algorithms using fusion of artificial intelligence methods to predict tar concentration. Outputs of development are three fuzzy structures optimized with genetic algorithms resulting in genetic algorithm (GA)-FUZZY, GA-adaptive neuro fuzzy inference system (ANFIS), GA-GA-FUZZY algorithms. Proposed algorithms are used for the tar prediction in the cigarette production process. The results of prediction are compared with gas chromatograph (high-performance liquid chromatography (HPLC)) readings.

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Author:Muamer Kafadar, Zikrija Avdagic, Lejla Begic Fazlic
URN:urn:nbn:de:hbz:tr5-1396
DOI:https://doi.org/10.2991/ijcis.d.191122.001
Parent Title (English):International Journal of Computational Intelligence Systems
Publisher:Atlantis Press
Document Type:Article (specialist journals)
Language:English
Date of OPUS upload:2022/09/15
Date of first Publication:2019/12/03
Publishing University:Hochschule Trier
Release Date:2022/09/15
Tag:GA-ANFIS; GA-FUZZY; GA-GA-FUZZY; adaptive neuro fuzzy system; fuzzy logic; genetic algorithm; tar
GND Keyword:Fuzzy-Logik; Neuro-Fuzzy-System; Genetischer Algorithmus; Zigarettenrauch; Teer
Volume:12
Issue:2
Page Number:15
First Page:1497
Last Page:1511
Departments:FB Umweltplanung/-technik (UCB)
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme
5 Naturwissenschaften und Mathematik / 54 Chemie
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International