{"title":"Supercapacitor module quality prewarning based on the improved whale optimization algorithm and GM (1,1) gray prediction model","authors":"Baochen Liu, Xiaobang Sun, Conghao Liu, Jun Xiang","doi":"10.61416/ceai.v25i2.8343","DOIUrl":null,"url":null,"abstract":"This paper proposes an algorithm combining the improved whale optimization algorithm (WOA) and GM (1,1) to predict the number of qualified products in the test batches of supercapacitor modules. Based on this algorithm, a quality prewarning system is developed on LabVIEW software. First, the GM (1,1) gray prediction model is established with the number of qualified products in the test batch as the original sequence, and then a nonlinear iterative parameter is proposed to improve the WOA. The improved WOA is used to optimize the background value sequence in the GM (1,1) gray prediction model, to obtain the optimal prediction algorithm. Then, on the LabVIEW software platform, the simulation of the GM (1,1) gray prediction model, the algorithm combining WOA and GM (1,1) and the algorithm combining the improved WOA and GM (1,1) are carried out for fifty groups of original data series. Taking one group of simulation data results as an example, the relative error of the prediction value of the algorithm combining the improved WOA and GM (1,1) is 0.0004%, which is better than 0.0112% of the algorithm combining the WOA and GM (1,1) and 1.5429% of the GM (1,1) gray prediction model. Finally, the quality prewarning system of the supercapacitor module is developed by using LabVIEW software, which provides a more accurate quality prewarning function for the test process of the supercapacitor module. DOI: 10.61416/ceai.v25i2.8343","PeriodicalId":50616,"journal":{"name":"Control Engineering and Applied Informatics","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Control Engineering and Applied Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.61416/ceai.v25i2.8343","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes an algorithm combining the improved whale optimization algorithm (WOA) and GM (1,1) to predict the number of qualified products in the test batches of supercapacitor modules. Based on this algorithm, a quality prewarning system is developed on LabVIEW software. First, the GM (1,1) gray prediction model is established with the number of qualified products in the test batch as the original sequence, and then a nonlinear iterative parameter is proposed to improve the WOA. The improved WOA is used to optimize the background value sequence in the GM (1,1) gray prediction model, to obtain the optimal prediction algorithm. Then, on the LabVIEW software platform, the simulation of the GM (1,1) gray prediction model, the algorithm combining WOA and GM (1,1) and the algorithm combining the improved WOA and GM (1,1) are carried out for fifty groups of original data series. Taking one group of simulation data results as an example, the relative error of the prediction value of the algorithm combining the improved WOA and GM (1,1) is 0.0004%, which is better than 0.0112% of the algorithm combining the WOA and GM (1,1) and 1.5429% of the GM (1,1) gray prediction model. Finally, the quality prewarning system of the supercapacitor module is developed by using LabVIEW software, which provides a more accurate quality prewarning function for the test process of the supercapacitor module. DOI: 10.61416/ceai.v25i2.8343
期刊介绍:
The Journal is promoting theoretical and practical results in a large research field of Control Engineering and Technical Informatics. It has been published since 1999 under the Romanian Society of Control Engineering and Technical Informatics coordination, in its quality of IFAC Romanian National Member Organization and it appears quarterly.
Each issue has up to 12 papers from various areas such as control theory, computer engineering, and applied informatics. Basic topics included in our Journal since 1999 have been time-invariant control systems, including robustness, stability, time delay aspects; advanced control strategies, including adaptive, predictive, nonlinear, intelligent, multi-model techniques; intelligent control techniques such as fuzzy, neural, genetic algorithms, and expert systems; and discrete event and hybrid systems, networks and embedded systems. Application areas covered have been environmental engineering, power systems, biomedical engineering, industrial and mobile robotics, and manufacturing.