Multi-Innovation Stochastic Gradient Identification Algorithm for Hammerstein Controlled Autoregressive Autoregressive Systems Based on the Key Term Separation Principle and on the Model Decomposition

Table 1

The SG parameter estimates and errors.

𝑡	𝑎1	𝑎2	𝑏1	𝑏2	𝛾1	𝛾2	𝛾3	𝑐1	𝑐2	𝛿 (%)
100	0.49020	−0.49380	0.01083	0.01505	0.00905	0.01482	0.02428	−0.00163	−0.00411	95.19650
200	0.49438	−0.49611	0.01052	0.01544	0.00908	0.01488	0.02437	−0.00159	−0.00415	95.15025
500	0.50037	−0.50067	0.01024	0.01579	0.00910	0.01491	0.02443	−0.00156	−0.00418	95.11481
1000	0.50136	−0.50130	0.01017	0.01588	0.00911	0.01492	0.02444	−0.00156	−0.00418	95.10595
2000	0.50017	−0.50007	0.01016	0.01589	0.00911	0.01492	0.02444	−0.00156	−0.00418	95.10507
3000	0.50042	−0.50031	0.01016	0.01589	0.00911	0.01492	0.02444	−0.00156	−0.00418	95.10474
True values	1.80000	0.80000	0.50000	0.65000	1.00000	0.50000	0.25000	0.30000	0.20000