Intelligent control of bulk tobacco curing schedule using LS-SVM-and ANFIS-based multi-sensor data fusion approaches

doi:10.3390/s19081778

	Intelligent control of bulk tobacco curing schedule using LS-SVM-and ANFIS-based multi-sensor data fusion approaches
	Wu, Juan1,2 ; Yang, Simon X.3
	2019-04-02
发表期刊	SENSORS
ISSN	1424-8220
卷号	19 期号:8
摘要	The bulk tobacco flue-curing process is followed by a bulk tobacco curing schedule, which is typically pre-set at the beginning and might be adjusted by the curer to accommodate the need for tobacco leaves during curing. In this study, the controlled parameters of a bulk tobacco curing schedule were presented, which is significant for the systematic modelling of an intelligent tobacco flue-curing process. To fully imitate the curer’s control of the bulk tobacco curing schedule, three types of sensors were applied, namely, a gas sensor, image sensor, and moisture sensor. Feature extraction methods were given forward to extract the odor, image, and moisture features of the tobacco leaves individually. Three multi-sensor data fusion schemes were applied, where a least squares support vector machines (LS-SVM) regression model and adaptive neuro-fuzzy inference system (ANFIS) decision model were used. Four experiments were conducted from July to September 2014, with a total of 603 measurement points, ensuring the results’ robustness and validness. The results demonstrate that a hybrid fusion scheme achieves a superior prediction performance with the coefficients of determination of the controlled parameters, reaching 0.9991, 0.9589, and 0.9479, respectively. The high prediction accuracy made the proposed hybrid fusion scheme a feasible, reliable, and effective method to intelligently control over the tobacco curing schedule. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.
关键词	Curing Electronic nose Fuzzy inference Fuzzy neural networks Fuzzy systems Moisture control Support vector machines Support vector regression Tobacco Adaptive neuro-fuzzy inference system Controlled parameter Curing schedule Feature extraction methods Least squares support vector machines Multisensor data fusion Prediction accuracy Prediction performance
DOI	10.3390/s19081778
收录类别	EI ; SCIE
语种	英语
WOS研究方向	Chemistry ; Engineering ; Instruments & Instrumentation
WOS类目	Chemistry, Analytical ; Engineering, Electrical & Electronic ; Instruments & Instrumentation
WOS记录号	WOS:000467644500032
出版者	MDPI AG
EI入藏号	20192106950908
EI分类号	723 Computer Software, Data Handling and Applications ; 731.3 Specific Variables Control ; 801 Chemistry ; 802.2 Chemical Reactions ; 821.4 Agricultural Products ; 961 Systems Science
原始文献类型	Journal article (JA)
出版地	BASEL
引用统计	被引频次：13[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://ir.cqcet.edu.cn/handle/39TD4454/3187
专题	电子与物联网学院
作者单位	1.School of Microelectronics and Communication Engineering, Chongqing University, Chongqing; 400044, China; 2.Chongqing College of Electronic Engineering, Chongqing; 401331, China; 3.School of Engineering, University of Guelph, Guelph; ON; N1G 2W1, Canada
第一作者单位	重庆电子科技职业大学
推荐引用方式 GB/T 7714	Wu, Juan,Yang, Simon X.. Intelligent control of bulk tobacco curing schedule using LS-SVM-and ANFIS-based multi-sensor data fusion approaches[J]. SENSORS,2019,19(8).
APA	Wu, Juan,&Yang, Simon X..(2019).Intelligent control of bulk tobacco curing schedule using LS-SVM-and ANFIS-based multi-sensor data fusion approaches.SENSORS,19(8).
MLA	Wu, Juan,et al."Intelligent control of bulk tobacco curing schedule using LS-SVM-and ANFIS-based multi-sensor data fusion approaches".SENSORS 19.8(2019).

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