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

Table 1

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

	The proposed method					The method in [18]
	1000	1.2829	−0.3333	0.04046	0.0312	0.0463	1.2206	−0.2576	0.03881	0.0294	0.1204
1500	1.2937	−0.3465	0.04243	0.0319	0.0341	1.2310	−0.2660	0.04077	0.0307	0.1080
2000	1.3044	−0.3668	0.04270	0.0318	0.0174	1.2508	−0.2892	0.03945	0.0292	0.0859
2500	1.3108	−0.37960.04317	0.0321	0.0069	1.2497	−0.3004	0.03994	0.0306	0.0795
3000	1.3051	−0.3837	0.04537	0.0329	0.0076	1.2452	−0.2948	0.04043	0.0309	0.0848
3500	1.3106	−0.3886	0.04387	0.0326	0.0026	1.2519	−0.3050	0.03951	0.0296	0.0760
4000	1.3088	−0.3874	0.04428	0.0325	0.0040	1.2653	−0.3211	0.03799	0.0290	0.0608
True	1.3139	−0.3886	0.04308	0.0315	0	1.3139	−0.3886	0.04308	0.0315	0
1000	1.3571	−0.4624	0.04853	0.0332	0.0625	1.2116	−0.2357	0.04010	0.0355	0.1372
1500	1.3879	−0.4842	0.04813	0.0291	0.0564	1.2484	−0.3041	0.04230	0.0356	0.0781
2000	1.3657	−0.4458	0.04578	0.0284	0.0564	1.2484	−0.3041	0.04230	0.0356	0.0781
2500	1.3526	−0.4311	0.04367	0.0292	0.0419	1.2645	−0.3127	0.04389	0.0348	0.0661
3000	1.3376	−0.4124	0.04375	0.0299	0.0245	1.2572	−0.2923	0.04404	0.0336	0.0815
3500	1.3205	−0.3900	0.04372	0.0307	0.0049	1.2643	−0.3020	0.04445	0.0339	0.0728
4000	1.3215	−0.3931	0.04462	0.0319	0.0066	1.2598	−0.2947	0.04617	0.0342	0.0791
True	1.3139	−0.3886	0.04308	0.0315	0	1.3139	−0.3886	0.04308	0.0315	0
1000	1.3992	−0.5185	0.04740	0.0281	0.1135	1.1806	−0.2297	0.04310	0.0360	0.1503
1500	1.3538	−0.4715	0.04400	0.0315	0.0671	1.2033	−0.2523	0.04599	0.0379	0.1208
2000	1.3469	−0.4702	0.04385	0.0320	0.0642	1.2176	−0.2774	0.04675	0.0379	0.1208
2500	1.3384	−0.4488	0.04311	0.0330	0.0474	1.2041	−0.2608	0.04882	0.0376	0.1230
3000	1.3001	−0.3976	0.04310	0.0339	0.0121	1.2190	−0.2733	0.05037	0.0374	0.1091
3500	1.3077	−0.4069	0.04492	0.0336	0.0142	1.2457	−0.3086	0.04919	0.0358	0.0769
4000	1.3081	−0.3999	0.04468	0.0336	0.0095	1.2304	−0.2865	0.04844	0.0368	0.0963
True	1.3139	−0.3886	0.04308	0.0315	0	1.3139	−0.3886	0.04308	0.0315	0