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

Table 3

The F-RLS estimates and errors in Example 2 ( and ).

									(%)
	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
	100	0.10227	−0.88895	0.29620	0.02428	−0.45977	1.39972	0.43173	23.55652
	200	0.10022	−0.94211	0.26126	0.06755	−0.30135	1.31216	0.41341	13.31668
	500	0.11893	−1.01753	0.24656	0.08815	−0.27093	1.24343	0.43981	7.40054
	1000	0.13195	−1.05047	0.25483	0.10514	−0.24217	1.26386	0.47397	5.67199
	2000	0.14551	−1.05983	0.25634	0.11672	−0.21751	1.22939	0.51038	3.02299
	3000	0.14632	−1.06041	0.25650	0.11459	−0.20918	1.22061	0.52227	2.68962
True values		0.14000	−1.05000	0.25000	0.12500	−0.19000	1.19000	0.50000