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时间:2017-08-11 数学毕业论文 我要投稿

摘 要
2 采用横向最小2乘拟合与纵向卡尔曼滤波方法的联合预测方法对数据表进行填充;
3 建立了灰色马尔柯夫预测模型,并对0205批次使用月数18时﹑0306批次使用月数9时和0310批次使用月数12时的千车故障数进行了预测。
0205批次使用月数18时的千车故障数为 79.65;
0306批次使用月数9时的千车故障数为  32.78;
0310批次使用月数12时的千车故障数为 12.57;
关键词: 最小2乘法;灰色预测;马尔柯夫链


Post-sale service data utilization model
This paper factories car a 1000 car parts fault of a few data tables for study , After has analyzed the data sheet characteristic and has rejected in the table the unreasonable data, Has established many models according to the data different characteristic, Has carried on the forecast to 3 productions raid of part quality condition, How discussed improved the data sheet to further to enhance the data the validity, And gave a series of constructive comment to the factory quality control.
The main work:
1 on the data analysis, The original 1000 car fault the definition of several unreasonable, And its amendments, Is a more reasonable number of 1,000 cars the concept of fault.
2 horizontal and vertical least squares fitting method of Kalman filtering method of predicting the data sheet filler
3 Gray established three Markov chain model. 0205 installment also use a few of 18:00 ﹑ 0,306 installment on the use of several 0900 and 0310 installments on the use of a few 2:00 fault of a few thousand cars a prediction.
Forecast results:
0,205 batches of 18 for the use of 1,000 cars at 79.65 Fault
0,306 installment on the use of the 0900 number 1000 Trouble at 32.78
0,310 installment on the use of several 1000 12:00 Trouble at 12.57
4 after-use method to predict the test data
5 Finally, the re-tabulation of the proposals
Keywords: least squares; Gray forecast; markov

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