Table 1 Performance comparison of noise elimination using five different algorithms: Least mean squares (LMS), Recursive least squares (RLS), Kalman filter, Long short-term memory network (LSTM), and Enhanced LSTM.
From: Advancements in noise reduction for wheel speed sensing using enhanced LSTM models
| Â | LMS | RLS | Kalman filter | LSTM | Enhanced LSTM |
|---|---|---|---|---|---|
MSE | 1.938626 | 1.930258 | 0.087108 | 0.039759 | 0.000662 |
RMSE | 1.392345 | 1.389337 | 0.295141 | 0.140902 | 0.010640 |
SNR | 12.166664 dB | 11.865727 dB | 10.602338 dB | 14.008629 dB | 31.797619 dB |
MAE | 1.051012 | 1.056992 | 0.226804 | 0.140902 | 0.010640 |
PSNR | -2.874940 dB | -2.856153 dB | 10.599401 dB | 21.546593 dB | 46.325974 dB |
R | 0.969277 | 0.966902 | 0.955583 | 0.980656 | 0.999507 |
