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Table 1 Performance comparison of LMS, APA, KAPA and KFAPA for X-component of Lorenz series prediction with different noise levels

From: Kernel fractional affine projection algorithm

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