Name
Abstract | ||
|---|---|---|
The modern industry data is characterized by large volume, large variety, low density value and high processing velocity. Hence, it is difficult to use industry big data for effectively analyzing the trend and the production state by traditional methods. Aiming to solve a problem, a technology platform and data processing framework are established, and the LS-ANN (least square-artificial neural network) method is applied to process the industry big data by analyzing the corresponding technological process and the working principle. By efficiently processing the time series data, this method gives the industry production process with the ability of self-adaptation and fault tolerance. The effectiveness of the proposed method is demonstrated by experimental simulations. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/10798587.2017.1316074 | INTELLIGENT AUTOMATION AND SOFT COMPUTING |
Keywords | Field | DocType |
Trend prediction, Industry big data, LS-ANN | Time series,Data mining,Data processing,Computer science,Scheduling (production processes),Fault tolerance,Artificial neural network,Trend prediction,Big data,Low density | Journal |
Volume | Issue | ISSN |
23 | 4 | 1079-8587 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junlin Qiu | 1 | 2 | 1.38 |
| Chenming Li | 2 | 4 | 1.09 |
| Lei Qiu | 3 | 0 | 0.34 |
| Hui Liu | 4 | 0 | 0.68 |
| Xu Lizhong | 5 | 155 | 24.51 |