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- W2165624728 abstract "The problem of system identification is considered for single-input-single-output stable linear discrete- and continuous-time time-invariant systems. Worst-case asymptotic optimal or suboptimal identification methods are sought. The main objective of system identification is to reduce uncertainty of a system via input-output observations. One possibility of modeling uncertainty sets is to use in -nets. The concepts of in -nets and in -dimension are studied and their relationships to system identifiability are exploited. The problem of system identification for discrete-time systems is considered. It is shown that for the algebra of exponentially stable systems, truncation of system kernels leads naturally to a minimal in -net. It is demonstrated that finding an asymptotically optimal identification algorithm can be reduced to a minimax problem which can be solved using certain standard mathematical programming algorithms. Properties of the minimax problem are derived. The identification of continuous-time systems via sampling is considered. >" @default.
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- W2165624728 date "2002-12-09" @default.
- W2165624728 modified "2023-09-24" @default.
- W2165624728 title "Worst case identification in discrete and continuous time systems" @default.
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- W2165624728 doi "https://doi.org/10.1109/cdc.1991.261464" @default.
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