The stability of the moisture content of the cigarette is an important index to evaluate the quality of the cigarette. The cooling moisture content after cut tobacco drying process is a key factor affecting the stability of the moisture content of the cigarette. In order to realize its accurate prediction and ensure the stability, in Honghe cigarette factory, a cooling moisture content prediction model is built based on a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm via the historical production data. Besides, the proposed PSO-ELM algorithm is also compared with multiple linear regression (MLR), support vector machine (SVM) and the traditional extreme learning machine (ELM) algorithms in the same data set on the prediction. The prediction accuracy of PSO-ELM method is the highest and the average error of the prediction standard is the lowest. The results indicated the proposed method can achieve a better prediction performance over compared methods and it provides a new method to realize the prediction of the cooling moisture content after cut tobacco drying process.
Citation: Ming Zhu, Kai Wu, Yuanzhen Zhou, Zeyu Wang, Junfeng Qiao, Yong Wang, Xing Fan, Yonghong Nong, Wenhua Zi. Prediction of cooling moisture content after cut tobacco drying process based on a particle swarm optimization-extreme learning machine algorithm[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2496-2507. doi: 10.3934/mbe.2021127
The stability of the moisture content of the cigarette is an important index to evaluate the quality of the cigarette. The cooling moisture content after cut tobacco drying process is a key factor affecting the stability of the moisture content of the cigarette. In order to realize its accurate prediction and ensure the stability, in Honghe cigarette factory, a cooling moisture content prediction model is built based on a particle swarm optimization-extreme learning machine (PSO-ELM) algorithm via the historical production data. Besides, the proposed PSO-ELM algorithm is also compared with multiple linear regression (MLR), support vector machine (SVM) and the traditional extreme learning machine (ELM) algorithms in the same data set on the prediction. The prediction accuracy of PSO-ELM method is the highest and the average error of the prediction standard is the lowest. The results indicated the proposed method can achieve a better prediction performance over compared methods and it provides a new method to realize the prediction of the cooling moisture content after cut tobacco drying process.
[1] | C. L. Yuan, W. Yi, Y. Bin, Relationship between Quality of dried cut tobacco and cooling temperature, Tob. Sci. Technol., 36 (2003), 9-12. |
[2] | G. B. Huang, Q. Y. Zhu, C. K. Siew, Extreme learning machine: theory and applications, Neurocomputing, 70 (2006), 489-501. doi: 10.1016/j.neucom.2005.12.126 |
[3] | G. B. Huang, H. M. Zhou, X. J. Ding, R. Zhang, Extreme learning machine for regression and multiclass classification, IEEE Trans. Syst. Man. Cybern. B, 42 (2010), 513-529. |
[4] | M. V. Heeswijk, Y. Miche, E. Oja, A. Lendasse, GPU-accelerated and parallelized ELM ensembles for large-scale regression, Neurocomputing, 74(2011), 2430-2437. doi: 10.1016/j.neucom.2010.11.034 |
[5] | Y. Miche, A. Akusok, D. Veganzones, K. M. Björk, E. Séverin, P. Du Jardin, et al., SOM-ELM-Self-Organized Clustering using ELM, Neurocomputing, 165 (2015), 238-254. doi: 10.1016/j.neucom.2015.03.014 |
[6] | Y. Jin, J. Li, C.Y. Lang, Q. Ruan, Multi-task clustering ELM for VIS-NIR cross-modal feature learning, Multidimens. Syst. Signal Process., 28 (2017), 905-920. doi: 10.1007/s11045-016-0401-8 |
[7] | H. Zhong, C. Miao, Z. Shen, Y. Feng, Comparing the learning effectiveness of BP, ELM, I-ELM, and SVM for corporate credit ratings, Neurocomputing, 128 (2014), 285-295. doi: 10.1016/j.neucom.2013.02.054 |
[8] | G. B. Huang, L. Chen, C. K. Siew, Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks, 174 (2006), 879-892. |
[9] | G. B. Huang, C. Lei, Convex incremental extreme learning machine, Neurocomputing, 70 (2007), 3056-3062. doi: 10.1016/j.neucom.2007.02.009 |
[10] | G. B. Huang, C. Lei, Enhanced random search based incremental extreme learning machine, Neurocomputing, 71(2008), 3460-3468. doi: 10.1016/j.neucom.2007.10.008 |
[11] | X. M. Wang, X. H. Wan, Y. Y. Zhu, Z. L. Jiang, J. X. Liu, Prediction for Building Vibration Velocity Caused by Blasting Based on PSO-ELM, Sci. Technol. Rev., 32 (2014), 15-20. |
[12] | A. Banan, A. Nasiri, A. Taheri-Garavand, Deep learning-based appearance features extraction for automated carp species identification, Aquacult. Eng., 89 (2020), 102053. doi: 10.1016/j.aquaeng.2020.102053 |
[13] | R. Taormina, K.W. Chau, ANN-based interval forecasting of streamflow discharges using the LUBE method and MOFIPS, Eng. Appl. Artif. Intel., 45 (2015), 429-440. doi: 10.1016/j.engappai.2015.07.019 |
[14] | S. F. Ardabili, B. Najafi, S. Shamshirband, B. M. Bidgoli, R. C. Deo, K. W. Chau, Computational intelligence approach for modeling hydrogen production: a review, Eng. Appl. Comput. Fluid, 12 (2018), 438-458. |
[15] | C. L. Wu, K. W. Chau, Prediction of rainfall time series using modular soft computing methods, Eng. Appl. Artif. Intel., 26 (2013), 997-1007. doi: 10.1016/j.engappai.2012.05.023 |
[16] | C. Cheng, W. Niu, Z. Feng, J. Shen, K. Chau, Daily reservoir runoff forecasting method using artificial neural network based on quantum-behaved particle swarm optimization, Water, 7 (2015), 4232-4246. doi: 10.3390/w7084232 |
[17] | R. Taormina, K. W. Chau, Data-driven input variable selection for rainfall-runoff modeling using binary-coded particle swarm optimization and Extreme Learning Machines, J. Hydrol., 529 (2015), 1617-1632. doi: 10.1016/j.jhydrol.2015.08.022 |
[18] | H. Liang, J. Zou, Z. Li, M. J. Khan, Y. Lu, Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm, Future Gener. Comput. Syst., 95 (2019), 454-466. doi: 10.1016/j.future.2018.12.068 |
[19] | C. Shang, X. Huang, F. You, Data-driven robust optimization based on kernel learning, Comput. Chem. Eng., 106 (2017), 464-479. doi: 10.1016/j.compchemeng.2017.07.004 |
[20] | P. Goodwin, R. Lawton, On the asymmetry of the symmetric MAPE, Int. J. Forecast., 15 (1999), 405-408. doi: 10.1016/S0169-2070(99)00007-2 |
[21] | J. J. Da Costa, F. Chainet, B. Celse, M. Lacoue-Nègre, C. Ruckebusch, D. Espinat, Comparing kriging, spline, and MLR in product properties modelization: application to cloud point prediction, Energ Fuels, 32 (2018), 5623-5634. doi: 10.1021/acs.energyfuels.7b04067 |
[22] | A. Kavousi-Fard, Modeling uncertainty in tidal current forecast using prediction interval-based SVR, IEEE Trans. Sustainable Energy, 99 (2016), 1-3. |