Fuzzy System Based on Two-Step Cascade Genetic Optimization Strategy for Tobacco Tar Prediction

• Published in: International journal of computational intelligence systems Vol. 12; no. 2; pp. 1497 - 1511
• Main Authors: Kafadar, Muamer, Avdagic, Zikrija, Fazlic, Lejla Begic
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.01.2019; Springer Nature B.V; Springer
• Subjects: Adaptive algorithms; Adaptive neuro fuzzy system (ANFISs); Adaptive systems; Artificial intelligence; Artificial neural networks; Fuzzy logic; Fuzzy logic (FUZZY); GA-ANFIS; GA-FUZZY; Gas chromatography; Genetic algorithm (GA); Genetic algorithms; High-performance liquid chromatography; Optimization; Research Article; Tar; Tobacco; Genetic algorithm (GA); GA-FUZZY; Tar; Fuzzy logic (FUZZY); GA-GA-FUZZY; Adaptive neuro fuzzy system (ANFISs); GA-ANFIS
• ISSN: 1875-6891; 1875-6883; 1875-6883
• DOI: 10.2991/ijcis.d.191122.001

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.