Volume 12, Issue 2, 2019, Pages 1497 - 1511
Fuzzy System Based on Two-Step Cascade Genetic Optimization Strategy for Tobacco Tar Prediction
Authors
Muamer Kafadar1, *, Zikrija Avdagic1, Lejla Begic Fazlic2
1Faculty of Electrical Engineering, University of Sarajevo, Sarajevo, Bosnia and Herzegovina
2Trier University of Applied Sciences, Environmental Campus, Germany
*Corresponding author. Email: muamer.kafadar@gmail.com
Corresponding Author
Muamer Kafadar
Received 10 November 2019, Accepted 18 November 2019, Available Online 3 December 2019.
- DOI
- 10.2991/ijcis.d.191122.001How to use a DOI?
- Keywords
- Adaptive neuro fuzzy system (ANFISs); Genetic algorithm (GA); Fuzzy logic (FUZZY); Tar; GA-ANFIS; GA-FUZZY; GA-GA-FUZZY
- Abstract
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.
- Copyright
- © 2019 The Authors. Published by Atlantis Press SARL.
- Open Access
- This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).
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TY - JOUR AU - Muamer Kafadar AU - Zikrija Avdagic AU - Lejla Begic Fazlic PY - 2019 DA - 2019/12/03 TI - Fuzzy System Based on Two-Step Cascade Genetic Optimization Strategy for Tobacco Tar Prediction JO - International Journal of Computational Intelligence Systems SP - 1497 EP - 1511 VL - 12 IS - 2 SN - 1875-6883 UR - https://doi.org/10.2991/ijcis.d.191122.001 DO - 10.2991/ijcis.d.191122.001 ID - Kafadar2019 ER -