摘要
《辐射安全许可证》是我国境内核技术利用单位必不可少的资质证明,许可证的申请业务是具有随机波动特性的复杂灰色系统,利用传统的预测技术难以准确预测许可证申请业务的未来变化趋势。为解决上述问题,运用新陈代谢-GM(1,1)模型对S省的辐射安全许可证申请数量建模预测,并采用马尔科夫模型和残差修正法对预测序列进行修正。经仿真证明,新陈代谢模型可以提升近期数据对于未来的影响,更适合许可证申请业务的特性。马尔科夫模型和残差修正法可以有效提升模型预测精度。
Predicting applications for the License of Radiation Safety,which is an indispensable certificate for nuclear technology utilizing organizations in China,is significant to improve authority's business process.However,the applications fluctuate randomly,posing a challenge to accurately predict its trend with traditional methods.For this purpose,a metabolism-GM(1,1)model is built for predicting the applications of S Province.To improve the accuracy,the Markov Chain and residue-correcting method are used to modify the prediction sequence.With simulation experiment,the proposed methodology turns out to improve the effect of recent data,which is more suitable for license application.
作者
于浩洋
曾瑞
左敏
张青川
YU Hao-yang;ZENG Rui;ZUO Min;ZHANG Qing-chuan(School of Computer and Information Engineering,Beijing Technology and Business University,Beijing 100048,China;Nuclear and Radiation Safety Center,Ministry of Ecology and Environment of the People's Republic of China,Beijing 100082,China;National Engineering Laboratory for Agri-Product Quality Traceability,Beijing 100048,China;Beijing Key Laboratory of Big Data Technology for Food Safety,Beijing,100048,China)
出处
《计算机仿真》
北大核心
2020年第8期439-445,共7页
Computer Simulation
基金
国家重点研发计划(2016YFD0401205),国家自然科学基金(6187022251)。
关键词
辐射安全许可证
新陈代谢模型
灰色预测
马尔科夫模型
残差修正
License of radiation safety
Metabolism model
Grey prediction
Markov model
Residue-correcting method
