GA-ANFIS expert system prototype for detection of tar content in the manufacturing process

Fazlic, Lejla Begic; Avdagić, Zikrija; Besic, Ingmar

doi:10.1109/mipro.2015.7160457

Cited by 7 publications

(7 citation statements)

References 6 publications

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“…This paper extends the initial work [31] and shows better results in prediction accuracy than what has been presented in recently published papers.…”

Section: Introductionsupporting

confidence: 81%

“…In this work, we used ANFIS architecture [31] (four cigarette features as Inputs, one Output-concentration of tar, and sixteen rules) as it is presented in Figure 1.…”

Section: Anfis Architecturementioning

confidence: 99%

“…Combinatorial MF parameters algorithm GA-FUZZY optimizes FIS MF parameters using GA. It was developed in the first part of research [31], as it is shown in next steps:…”

Section: Descriptionmentioning

confidence: 99%

“…Our goal was to develop novel FS based of two-step cascade genetic optimization strategy (GA-GA-FUZZY) for tobacco tar prediction and get better results of prediction of tar level in a cigarette filter using only four cigarette features: diameter, filter ventilation, nicotine and carbon monoxide. In our previous research [31], the proposed combinatorial MF parameters algorithm combines the advantages of ANFIS [32] and GA [33,34]. In the first step ANFIS combinatorial mechanism generates six different fuzzy structures.…”

Section: Introductionmentioning

confidence: 99%

“…We compared our results with the amount of tar being result of the lab analysis through HPLC [1]. The performances of these algorithms-methods are also compared with our previous results based on ANFIS and GA and shows effectiveness of this new approach [31].…”

Section: Introductionmentioning

confidence: 99%

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

Kafadar¹,

Avdagić²,

Fazlic³

2019

IJCIS

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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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“…This paper extends the initial work [31] and shows better results in prediction accuracy than what has been presented in recently published papers.…”

Section: Introductionsupporting

confidence: 81%

“…In this work, we used ANFIS architecture [31] (four cigarette features as Inputs, one Output-concentration of tar, and sixteen rules) as it is presented in Figure 1.…”

Section: Anfis Architecturementioning

confidence: 99%

“…Combinatorial MF parameters algorithm GA-FUZZY optimizes FIS MF parameters using GA. It was developed in the first part of research [31], as it is shown in next steps:…”

Section: Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kafadar¹,

Avdagić²,

Fazlic³

2019

IJCIS

Self Cite

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Artificial Intelligence in Manufacturing

Chen

Wang

2022

Artificial Intelligence and Lean Manufacturing

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Künstliche Intelligenz in der Fertigung

Chen,

Wang

2023

Künstliche Intelligenz Und Schlanke Produktion

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GA-ANFIS expert system prototype for detection of tar content in the manufacturing process

Cited by 7 publications

References 6 publications

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

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

Artificial Intelligence in Manufacturing

Künstliche Intelligenz in der Fertigung

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