Table 1 Performance comparison of LMS, APA, KAPA and KFAPA for X-component of Lorenz series prediction with different noise levels
Algorithm | LMS | APA | KAPA | KFAPA |
|---|---|---|---|---|
Training MSE (σ = 0.05) | 0.02250 ± 1.75e−005 | 0.01454 ± 5.26e−004 | 0.041827 ± 1.93e−065 | 0.02156 ± 1.39e−005 |
Training MSE (σ = 0.05) | 0.01583 ± 0.22e−005 | 0.07820 ± 0.00306 | 0.017738 ± 2.28e−003 | 0.091538 ± 1.367e−004 |
Training MSE (σ = 0.02) | 0.01903 ± 0.003149 | 0.025175 ± 0.000198 | 0.001399 ± 0.000189 | 0.0020052 ± 6.50e−005 |
Training MSE (σ = 0.02) | 0.002970 ± 0.00170 | 0.005556 ± 0.0004324 | 0.001356 ± 0.000131 | 0.0027892 ± 9.49e−004 |
Training MSE (σ = 0.04) | 0.004349 ± 0.0003927 | 0.00680 ± 0.0007169 | 0.004117 ± 0.000251 | 0.0048219 ± 0.0001680 |
Training MSE (σ = 0.04) | 0.0049979 ± 0.0004128 | 0.007162 ± 0.0009767 | 0.005117 ± 0.000382 | 0.005822 ± 0.0003391 |
Training MSE (σ = 0.1) | 0.015863 ± 0.0009678 | 0.010932 ± 0.0025963 | 0.026628 ± 0.00094904 | 0.045301 ± 0.00086739 |
Training MSE (σ = 0.1) | 0.016166 ± 0.007296 | 0.019066 ± 0.0044241 | 0.035729 ± 0.00128194 | 0.0555606 ± 0.0023143 |
Training MSE (σ = 0.5) | 0.42356 ± 0.10011 | 0.50001 ± 0.010242 | 0.82530 ± 0.2115 | 0.4209 ± 0.030332 |
Training MSE (σ = 0.5) | 0.51752 ± 0.22074 | 0.69218 ± 0.028178 | 0.91918 ± 0.32231 | 0.52013 ± 0.047689 |