Name
Abstract | ||
|---|---|---|
According to the historical data of the x x factory the BP artificial neural network model is used, and a mapping relationship between the input and the output value of spare parts demand is set up. Model training results show that the model can better predict, with high accuracy. On this basis, this article forecasts the spare parts demand of the enterprise on 2008. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NCM.2009.31 | NCM |
Keywords | Field | DocType |
demand forecasting,improved bp neural network,output value,enterprise,high accuracy,bp artificial neural network,artificial neural network,backpropagation,spare parts,factory,bp network,improved bp artificial neural network,forecast demand,model training result,model training,maintenance engineering,mapping relationship,neural nets,historical data,spare parts demand,production facilities,neural network,forecasting,mathematical model,artificial neural networks,predictive models,data models | Artificial neural network model,Data modeling,Spare part,Demand forecasting,Factory,Computer science,Artificial intelligence,Artificial neural network,Backpropagation,Machine learning,Maintenance engineering | Conference |
ISBN | Citations | PageRank |
978-0-7695-3769-6 | 1 | 0.35 |
References | Authors | |
1 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiafu Ren | 1 | 1 | 1.03 |
| Min Xiao | 2 | 1 | 0.35 |
| Zongfang Zhou | 3 | 17 | 9.48 |
| Fang Zhang | 4 | 3 | 3.08 |