TY  - JOUR
A1  - Kafadar, Muamer
A1  - Avdagic, Zikrija
A1  - Begic Fazlic, Lejla
T1  - Fuzzy system based on two-step cascade genetic optimization strategy for tobacco tar prediction
T2  - International Journal of Computational Intelligence Systems
N2  - 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.
KW  - Fuzzy-Logik
KW  - Neuro-Fuzzy-System
KW  - Genetischer Algorithmus
KW  - Zigarettenrauch
KW  - Teer
KW  - adaptive neuro fuzzy system
KW  - genetic algorithm
KW  - fuzzy logic
KW  - tar
KW  - GA-ANFIS
KW  - GA-FUZZY
KW  - GA-GA-FUZZY
Y1  - 2019
UR  - https://hst.opus.hbz-nrw.de/frontdoor/index/index/docId/139
UR  - https://nbn-resolving.org/urn:nbn:de:hbz:tr5-1396
VL  - 12
IS  - 2
SP  - 1497
EP  - 1511
PB  - Atlantis Press
ER  -