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计算机系统应用英文版:2017,26(4):212-217
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基于马尔科夫模型的就餐人数预测
(1.福建师范大学 软件学院, 福州 350108;2.福建三明学院 现代教育技术中心, 三明 365004)
Repast Number Prediction Based on Markov Model
(1.Faculty of Software, Fujian Normal University, Fuzhou 350108, China;2.Center of Modern Education Technology, Sanming University, Sanming 365004, China)
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Received:August 01, 2016    Revised:August 29, 2016
中文摘要: 准确预测就餐人数能够降低学校食堂的运行成本,提高学生对食堂的满意度. 根据校园一卡通的消费情况,提出一种基于马尔科夫模型的就餐人数预测研究方法. 首先,通过计算早餐就餐行为得到初始概率;其次,分别通过计算早、午餐和午、晚餐就餐行为得到早餐午餐概率转移矩阵和午餐晚餐概率转移矩阵;最后,根据初始概率和概率转移矩阵构建的模型预测三餐的就餐人数. 该方法的就餐人数预测的平均预测误差率为1.31%,具有良好的预测效果. 实验结果表明,该方法能够反映学生的就餐行为,从而可以为学校后勤部门提供一些参考意见,有助于学校的建设和管理也有助于满足学生的需要.
Abstract:To predict the repast number accurately can reduce the cost of school canteen and improve students' satisfaction. A novel method based on Markov model to predict repast number is proposed according to the consumption situation of campus card system. Firstly, an initial probability is obtained by calculating the eating behavior of breakfast. Secondly, two transfer probability matrices are computed, one is the transfer probability between the behaviors of students having breakfast and having lunch; the other is the transfer probability between the behaviors of students having lunch and having supper. Finally, a Markov model is constructed according to the initial probability and the two probability transfer matrices to forecast the number of diners. The average prediction error of the proposed method is 1.31%, which has a good prediction performance. The experimental results show that the proposed Markov method can capture the students' dining behavior accurately. It may provide valuable information for the school logistics department, contribute to the construction and management of school and meet the needs of students better.
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基金项目:福建省自然科学基金(2014J01220);三明学院科研基金(B201201/G);福建省教育厅科技基金(JB13187)
引用文本:
徐彭娜,林志兴,林劼,江育娥.基于马尔科夫模型的就餐人数预测.计算机系统应用,2017,26(4):212-217
XU Peng-Na,LIN Zhi-Xing,LIN Jie,JIANG Yu-E.Repast Number Prediction Based on Markov Model.COMPUTER SYSTEMS APPLICATIONS,2017,26(4):212-217