Research Article

Correlation Analysis Algorithm-Based Multiple-Input Single-Output Wiener Model with Output Noise

Table 2

Identification results of the linear block in the second branch under different .

The proposed methodThe method in [18]

10001.5968−0.69840.02690.02300.08721.6151−0.76970.03990.02360.1312
15001.4904−0.49110.03220.02470.05661.6166−0.75310.03840.02420.1225
20001.5532−0.60550.03840.02350.02621.6164−0.76620.04280.02040.1374
25001.5523−0.60900.03590.02200.02711.6270−0.76680.04530.02190.1332
30001.5546−0.61510.03640.02280.03081.6070−0.72480.04240.02270.1046
35001.5092−0.56390.03700.02480.01221.6120−0.70920.03810.02260.0980
40001.5136−0.57150.03520.02450.00701.5787−0.64530.03590.02400.0543
True1.5218−0.57780.03050.025401.5218−0.57780.03050.02540

10001.4821−0.54520.03130.02960.03161.5969−0.77540.04060.02710.1400
15001.5003−0.57070.02980.02740.01401.5781−0.75810.04080.02890.1162
20001.4947−0.54520.02820.02570.02611.5701−0.73790.03970.02920.1029
25001.5278−0.59780.02910.02590.01281.5695−0.70460.03990.02960.0853
30001.5312−0.60730.03000.02650.01901.5422−0.67790.04100.02630.0631
35001.5381−0.60770.02700.02620.02101.5503−0.70090.04210.02530.0779
40001.5444−0.61360.02590.02440.02621.5477−0.70110.04140.02610.0777
True1.5218−0.57780.03050.025401.5218−0.57780.03050.02540

10001.4290−0.40600.03280.02670.11991.6486−0.77730.02590.02020.1520
15001.4438−0.42030.03040.02730.10801.6435−0.75600.02610.02100.1327
20001.4701−0.48300.03060.02820.06631.6275−0.75390.02790.02220.1262
25001.4441−0.46810.02750.02720.08261.6128−0.72480.02690.02230.1062
30001.4605−0.48750.02910.02790.06711.6134−0.71760.02290.02170.1028
35001.4787−0.53690.03240.02740.03651.6137−0.72320.02360.02190.1057
40001.4846−0.54240.03180.02730.03161.6224−0.73960.02610.02120.1171
True1.5218−0.57780.03050.025401.5218−0.57780.03050.02540