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

Table 2

The MRLS and F-MRLS estimates and errors in Example 1 ().

Algorithms								(%)
MRLS	100	−0.01946	−1.84785	0.44644	0.04712	−0.16528	0.39802	52.11950
	200	−0.03340	−1.60645	0.31299	0.05091	−0.14456	0.46017	34.45327
	500	0.03788	−1.39775	0.28600	0.03749	−0.14940	0.56178	18.20046
	1000	0.08074	−1.34458	0.28178	0.07949	−0.14078	0.61722	11.71737
	2000	0.11167	−1.28751	0.27285	0.10106	−0.13785	0.66411	6.24292
	3000	0.12600	−1.27455	0.26866	0.10656	−0.13729	0.68367	4.99772
F-MRLS	100	0.10919	−1.60279	0.39771	0.12515	−0.16795	0.46319	33.14292
	200	0.08155	−1.44298	0.31145	0.09590	−0.13875	0.51078	21.01533
	500	0.10144	−1.33934	0.25923	0.08705	−0.15442	0.60335	11.18663
	1000	0.12452	−1.30385	0.26703	0.11599	−0.14796	0.65282	7.04142
	2000	0.13930	−1.27241	0.26711	0.12703	−0.14550	0.69136	4.66378
	3000	0.14008	−1.25781	0.26567	0.11928	−0.14361	0.70371	4.03373
True values		0.13000	−1.21000	0.25000	0.13000	−0.14000	0.68000