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Extended Kalman Filter Method based on Backpropagation Neural Network in Current Sensor Online Calibration
| Content Provider | Scilit |
|---|---|
| Author | Zhao, Shuang Zhang, Hanzhu Liu, Jun Li, Yansong |
| Copyright Year | 2019 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering The signal collected by current sensor will contain various noises, which will have a negative impact on the its accuracy and calibration. In order to remove unwanted noises, an extended Kalman filter (EKF) method based on backpropagation (BP) neural network is proposed in this paper. BP neural network has good adaptive ability and non-linear mapping ability. EKF can effectively filter noise and improve the calibration accuracy for non-linear systems. The signal collected by current sensor is processed by EKF and is used as the input signal of BP neural network. The trained neural network can modify the output signal of EKF, so as to improve the calibration accuracy. The angle difference and ratio difference of current sensor calibration are below 0.1, which meets the national standard and shows the effectiveness of this method. |
| Related Links | https://iopscience.iop.org/article/10.1088/1757-899X/631/5/052038/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/631/5/052038 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 5 |
| Volume Number | 631 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2019-10-01 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Hardware and Architecture |
| Content Type | Text |
| Resource Type | Article |
