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1.	Hjalmarsson, Håkan, 1962- (författare) Optimally robust system identification of systems subject to amplitude-bounded stochastic disturbances 1998 Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286. ; 43:7, s. 947-953 Tidskriftsartikel (refereegranskat)abstract In this contribution it is shown that log cos(Ï€cursive Greek chi/(2C)) is the optimally robust criterion function for prediction error methods with respect to amplitude-bounded stochastic disturbances. This criterion function minimizes the maximum asymptotic covariance matrix of the parameter estimates for the family of innovations of the system which are amplitude bounded by the constant C. Furthermore, the stochastic worst case performance of the estimate corresponding to the criterion function log cos(Ï€cursive Greek chi/(2C)) is better than the worst case performance of the least squares estimate even if the constant C is chosen larger than the actual amplitude bound on the innovations. In addition to its favorable properties in a stochastic setting, this criterion function also generates estimates which are unfalsified in a deterministic framework.

Hjalmarsson, Håkan, 1962- (författare)
Optimally robust system identification of systems subject to amplitude-bounded stochastic disturbances
1998
Ingår i: IEEE Transactions on Automatic Control. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9286. ; 43:7, s. 947-953
Tidskriftsartikel (refereegranskat)abstract
- In this contribution it is shown that log cos(Ï€cursive Greek chi/(2C)) is the optimally robust criterion function for prediction error methods with respect to amplitude-bounded stochastic disturbances. This criterion function minimizes the maximum asymptotic covariance matrix of the parameter estimates for the family of innovations of the system which are amplitude bounded by the constant C. Furthermore, the stochastic worst case performance of the estimate corresponding to the criterion function log cos(Ï€cursive Greek chi/(2C)) is better than the worst case performance of the least squares estimate even if the constant C is chosen larger than the actual amplitude bound on the innovations. In addition to its favorable properties in a stochastic setting, this criterion function also generates estimates which are unfalsified in a deterministic framework.

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