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

Table 1

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

								(%)
	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
	100	0.09530	−1.72159	0.52726	0.13387	−0.15869	0.52551	42.21841
	200	0.03631	−1.52223	0.36553	0.07113	−0.11792	0.58022	25.56872
	500	0.07400	−1.40400	0.25492	0.04715	−0.16037	0.64763	15.50868
	1000	0.12015	−1.36311	0.27803	0.10606	−0.15335	0.68001	11.08080
	2000	0.14849	−1.31457	0.28056	0.12704	−0.15048	0.70624	7.98334
	3000	0.15005	−1.29138	0.27909	0.10998	−0.14680	0.71335	6.79480
True values		0.13000	−1.21000	0.25000	0.13000	−0.14000	0.68000