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 method | The method in [18] | | | | | | | | | | |
| | | | | | | | | | | | 1000 | 1.5968 | −0.6984 | 0.0269 | 0.0230 | 0.0872 | 1.6151 | −0.7697 | 0.0399 | 0.0236 | 0.1312 | 1500 | 1.4904 | −0.4911 | 0.0322 | 0.0247 | 0.0566 | 1.6166 | −0.7531 | 0.0384 | 0.0242 | 0.1225 | 2000 | 1.5532 | −0.6055 | 0.0384 | 0.0235 | 0.0262 | 1.6164 | −0.7662 | 0.0428 | 0.0204 | 0.1374 | 2500 | 1.5523 | −0.6090 | 0.0359 | 0.0220 | 0.0271 | 1.6270 | −0.7668 | 0.0453 | 0.0219 | 0.1332 | 3000 | 1.5546 | −0.6151 | 0.0364 | 0.0228 | 0.0308 | 1.6070 | −0.7248 | 0.0424 | 0.0227 | 0.1046 | 3500 | 1.5092 | −0.5639 | 0.0370 | 0.0248 | 0.0122 | 1.6120 | −0.7092 | 0.0381 | 0.0226 | 0.0980 | 4000 | 1.5136 | −0.5715 | 0.0352 | 0.0245 | 0.0070 | 1.5787 | −0.6453 | 0.0359 | 0.0240 | 0.0543 | True | 1.5218 | −0.5778 | 0.0305 | 0.0254 | 0 | 1.5218 | −0.5778 | 0.0305 | 0.0254 | 0 |
| | | | | | | | | | | | 1000 | 1.4821 | −0.5452 | 0.0313 | 0.0296 | 0.0316 | 1.5969 | −0.7754 | 0.0406 | 0.0271 | 0.1400 | 1500 | 1.5003 | −0.5707 | 0.0298 | 0.0274 | 0.0140 | 1.5781 | −0.7581 | 0.0408 | 0.0289 | 0.1162 | 2000 | 1.4947 | −0.5452 | 0.0282 | 0.0257 | 0.0261 | 1.5701 | −0.7379 | 0.0397 | 0.0292 | 0.1029 | 2500 | 1.5278 | −0.5978 | 0.0291 | 0.0259 | 0.0128 | 1.5695 | −0.7046 | 0.0399 | 0.0296 | 0.0853 | 3000 | 1.5312 | −0.6073 | 0.0300 | 0.0265 | 0.0190 | 1.5422 | −0.6779 | 0.0410 | 0.0263 | 0.0631 | 3500 | 1.5381 | −0.6077 | 0.0270 | 0.0262 | 0.0210 | 1.5503 | −0.7009 | 0.0421 | 0.0253 | 0.0779 | 4000 | 1.5444 | −0.6136 | 0.0259 | 0.0244 | 0.0262 | 1.5477 | −0.7011 | 0.0414 | 0.0261 | 0.0777 | True | 1.5218 | −0.5778 | 0.0305 | 0.0254 | 0 | 1.5218 | −0.5778 | 0.0305 | 0.0254 | 0 |
| | | | | | | | | | | | 1000 | 1.4290 | −0.4060 | 0.0328 | 0.0267 | 0.1199 | 1.6486 | −0.7773 | 0.0259 | 0.0202 | 0.1520 | 1500 | 1.4438 | −0.4203 | 0.0304 | 0.0273 | 0.1080 | 1.6435 | −0.7560 | 0.0261 | 0.0210 | 0.1327 | 2000 | 1.4701 | −0.4830 | 0.0306 | 0.0282 | 0.0663 | 1.6275 | −0.7539 | 0.0279 | 0.0222 | 0.1262 | 2500 | 1.4441 | −0.4681 | 0.0275 | 0.0272 | 0.0826 | 1.6128 | −0.7248 | 0.0269 | 0.0223 | 0.1062 | 3000 | 1.4605 | −0.4875 | 0.0291 | 0.0279 | 0.0671 | 1.6134 | −0.7176 | 0.0229 | 0.0217 | 0.1028 | 3500 | 1.4787 | −0.5369 | 0.0324 | 0.0274 | 0.0365 | 1.6137 | −0.7232 | 0.0236 | 0.0219 | 0.1057 | 4000 | 1.4846 | −0.5424 | 0.0318 | 0.0273 | 0.0316 | 1.6224 | −0.7396 | 0.0261 | 0.0212 | 0.1171 | True | 1.5218 | −0.5778 | 0.0305 | 0.0254 | 0 | 1.5218 | −0.5778 | 0.0305 | 0.0254 | 0 |
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