Filtering Based Recursive Least Squares Algorithm for Multi-Input Multioutput Hammerstein Models

Table 4

The comparison of parameter estimates and errors in Example 2 ().

Algorithms									(%)
MRLS	100	0.10379	−1.49022	0.30877	0.11454	−0.37596	0.96745	0.25108	34.46417
	200	0.08551	−1.31358	0.29070	0.10159	−0.30829	1.05130	0.24601	24.37082
	500	0.10950	−1.17837	0.27162	0.09651	−0.24919	1.13023	0.30548	14.80320
	1000	0.12218	−1.13050	0.26705	0.10410	−0.21956	1.17878	0.36159	9.76634
	2000	0.13294	−1.09517	0.26234	0.11047	−0.20567	1.18593	0.42481	5.36992
	3000	0.13715	−1.08439	0.25990	0.11297	−0.20141	1.18517	0.45571	3.49648
F-MRLS	100	0.12373	−1.02307	0.27283	0.10358	−0.39300	1.25093	0.32512	16.33820
	200	0.12306	−1.03981	0.24977	0.11748	−0.29306	1.19925	0.35407	10.57344
	500	0.13109	−1.05853	0.24655	0.11563	−0.25013	1.19098	0.40948	6.44687
	1000	0.13728	−1.06400	0.25207	0.12027	−0.22484	1.21159	0.45612	3.63495
	2000	0.14372	−1.06203	0.25315	0.12346	−0.20822	1.20110	0.49970	1.46876
	3000	0.14389	−1.05999	0.25321	0.12156	−0.20281	1.19897	0.51458	1.43078
True values		0.14000	−1.05000	0.25000	0.12500	−0.19000	1.19000	0.50000