DocumentCode :
433978
Title :
A recursive algorithm for MIMO stochastic model estimation
Author :
Agüero, Juan C. ; Goodwin, Graham C.
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Newcastle Univ., NSW, Australia
Volume :
3
fYear :
2004
fDate :
20-23 July 2004
Firstpage :
1658
Abstract :
Multivariable system identification is known to be a difficult problem. In part, this is due to the fact that, in general, the likelihood function is non-convex. The most commonly used class of procedures for off-line identification of multivariable systems is the method commonly known as sub-space. These methods avoid the non-convexity issue by using a multi-step procedure, which includes a singular value decomposition. Unfortunately, it is not easy to develop a recursive form of these sub-space algorithms due to the singular value decomposition step. Here, we borrow ideas from the sub-space methodologies to develop a novel recursive algorithm. We assume that the Kronecker invariants for the system are known. We also illustrate the performance of the algorithm via a simple example.
Keywords :
MIMO systems; identification; recursive estimation; singular value decomposition; stochastic processes; MIMO stochastic model estimation; multivariable system identification; recursive algorithm; singular value decomposition; sub-space algorithm; MIMO; Noise measurement; Observability; Parameter estimation; Recursive estimation; Robustness; Singular value decomposition; State estimation; State-space methods; Stochastic processes;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference, 2004. 5th Asian
Conference_Location :
Melbourne, Victoria, Australia
Print_ISBN :
0-7803-8873-9
Type :
conf
Filename :
1426889
Link To Document :
بازگشت