Name
Abstract | ||
|---|---|---|
Several methods (e.g. Move Average, Gene Expression Programming) can be used in safety risk monitoring of Hydraulic Power Generation Industrial Control Network. However, it is difficult for a single forecasting model to predict accurately. Based on the idea of ensemble learning algorithm, this paper gives the positive answer of above problem. This paper proposes an asymmetric error cost function (AEC) to evaluate prediction errors. The effect of the algorithm is confirmed. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/BigDataService.2019.00041 | 2019 IEEE Fifth International Conference on Big Data Computing Service and Applications (BigDataService) |
Keywords | Field | DocType |
ensemble learning,safety risk monitoring,asymmetric error cost function | Data mining,Gene expression programming,Hydraulic machinery,Computer science,Artificial intelligence,Control network,Ensemble learning,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-7281-0060-9 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengzhi Chen | 1 | 0 | 0.34 |
| Jun Hou | 2 | 4 | 4.14 |
| Li Qian-Mu | 3 | 33 | 14.78 |