Prediction modeling of cigarette ventilation rate based on genetic algorithm backpropagation (GABP) neural network

The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventil...

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Published in:	EURASIP journal on advances in signal processing Vol. 2024; no. 1; pp. 25 - 14
Main Authors:	Wei, Jiaxin, Wang, Zhengwei, Li, Shufang, Wang, Xiaoming, Xu, Huan, Wang, Xiushan, Yao, Sen, Song, Weimin, Wang, Youwei, Mei, Chao
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 22.02.2024 Springer Nature B.V SpringerOpen
Subjects:	Artificial neural networks Back propagation Back propagation networks BP neural network Cigarettes Design improvements Design parameters Engineering Genetic algorithms Genetics algorithm Multiple linear regression Neural networks Prediction models Predictions Quantum Information Technology Signal Processing and Machine Learning in Autonomous Systems Signal,Image and Speech Processing Spintronics Stability Technical services Tobacco Ventilation Ventilation rate Tobacco BP neural network Genetics algorithm Multiple linear regression Ventilation rate
ISSN:	1687-6180, 1687-6172, 1687-6180
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Abstract	The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight ( P < 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R 2 = 0.841) and the BPNN models’ (RMSE = 0.640, R 2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R 2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes.
AbstractList	The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight ( P < 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R 2 = 0.841) and the BPNN models’ (RMSE = 0.640, R 2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R 2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes. The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight (P < 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R2 = 0.841) and the BPNN models’ (RMSE = 0.640, R2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes. Abstract The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may experience unstable ventilation rates, which can lead to a decrease in cigarette quality and pose certain risks to smokers. By establishing the ventilation rate prediction model, guide the design of unit parameters in advance, to achieve the goal of stabilizing unit ventilation rate, improve the stability of cigarette ventilation rate, and enhance the quality of cigarettes. This paper used multiple linear regression networks (MLR), backpropagation neural networks (BPNN), and genetic algorithm-optimized backpropagation (GABP) to construct a model for the prediction of cigarette ventilation rate. The model results indicated that the total ventilation rate was significantly positively correlated with weight (P < 0.01), circumference, hardness, filter air permeability, and open resistance. The results showed that the MLR models' (RMSE = 0.652, R 2 = 0.841) and the BPNN models’ (RMSE = 0.640, R 2 = 0.847) prediction ability were limited. Optimization by genetic algorithm, GABP models were generated and exhibited a little better prediction performance (RMSE = 0.606, R 2 = 0.873). The results indicated that the GABP model has the highest accuracy in the prediction of predicting ventilation rate and can accurately predict cigarette ventilation rate. This method can provide theoretical guidance and technical support for the stability study of the ventilation rate of the unit, improve the design and manufacturing capabilities and product quality of short cigarette products, and help to improve the quality of cigarettes.
ArticleNumber	25
Author	Xu, Huan Wei, Jiaxin Wang, Zhengwei Wang, Xiushan Wang, Youwei Li, Shufang Song, Weimin Mei, Chao Yao, Sen Wang, Xiaoming
Author_xml	– sequence: 1 givenname: Jiaxin surname: Wei fullname: Wei, Jiaxin organization: Xuchang Cigarette Factory of Henan Zhongyan Industry Co., Ltd – sequence: 2 givenname: Zhengwei surname: Wang fullname: Wang, Zhengwei organization: Xuchang Cigarette Factory of Henan Zhongyan Industry Co., Ltd – sequence: 3 givenname: Shufang surname: Li fullname: Li, Shufang organization: Xuchang Cigarette Factory of Henan Zhongyan Industry Co., Ltd – sequence: 4 givenname: Xiaoming surname: Wang fullname: Wang, Xiaoming organization: Xuchang Cigarette Factory of Henan Zhongyan Industry Co., Ltd – sequence: 5 givenname: Huan orcidid: 0009-0003-1002-0785 surname: Xu fullname: Xu, Huan email: xh990321@163.com organization: College of Mechanical and Electrical Engineering, Henan Agricultural University – sequence: 6 givenname: Xiushan surname: Wang fullname: Wang, Xiushan organization: College of Mechanical and Electrical Engineering, Henan Agricultural University – sequence: 7 givenname: Sen surname: Yao fullname: Yao, Sen organization: College of Mechanical and Electrical Engineering, Henan Agricultural University – sequence: 8 givenname: Weimin surname: Song fullname: Song, Weimin organization: Technology Center, China Tobacco Henan Industrial Co., Ltd – sequence: 9 givenname: Youwei surname: Wang fullname: Wang, Youwei organization: College of Mechanical and Electrical Engineering, Henan Agricultural University – sequence: 10 givenname: Chao surname: Mei fullname: Mei, Chao organization: College of Mechanical and Electrical Engineering, Henan Agricultural University
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Snippet	The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may... Abstract The ventilation rate of cigarettes is an important indicator that affects the internal quality of cigarettes. When producing cigarettes, the unit may...
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SubjectTerms	Artificial neural networks Back propagation Back propagation networks BP neural network Cigarettes Design improvements Design parameters Engineering Genetic algorithms Genetics algorithm Multiple linear regression Neural networks Prediction models Predictions Quantum Information Technology Signal Processing and Machine Learning in Autonomous Systems Signal,Image and Speech Processing Spintronics Stability Technical services Tobacco Ventilation Ventilation rate
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Title	Prediction modeling of cigarette ventilation rate based on genetic algorithm backpropagation (GABP) neural network
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Volume	2024
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