Performance analysis of multi-innovation gradient type identification methods

It is well-known that the stochastic gradient (SG) identification algorithm has poor convergence rate. In order to improve the convergence rate, we extend the SG algorithm from the viewpoint of innovation modification and present multi-innovation gradient type identification algorithms, including a...

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Vydáno v:Automatica (Oxford) Ročník 43; číslo 1; s. 1 - 14
Hlavní autoři: Ding, Feng, Chen, Tongwen
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 2007
Témata:
Convergence properties
Forgetting factors
Parameter estimation
Recursive identification
Stochastic gradient
Stochastic processes
Recursive identification
Parameter estimation
Forgetting factors
Stochastic processes
Stochastic gradient
Convergence properties
ISSN:0005-1098, 1873-2836
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Shrnutí:It is well-known that the stochastic gradient (SG) identification algorithm has poor convergence rate. In order to improve the convergence rate, we extend the SG algorithm from the viewpoint of innovation modification and present multi-innovation gradient type identification algorithms, including a multi-innovation stochastic gradient (MISG) algorithm and a multi-innovation forgetting gradient (MIFG) algorithm. Because the multi-innovation gradient type algorithms use not only the current data but also the past data at each iteration, parameter estimation accuracy can be improved. Finally, the performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2006.07.024