A linear interpolatory algorithm for robust system identification with corrupted measurement data (original) (raw)

IEEE Transactions on Automatic Control, 1993

Abstract

A linear, robustly convergent interpolatory algorithm for system identification in the presence of bounded noise is presented. The algorithm converges in the actual, but unknown, system in the frequency domain in the noise-free case and maintains the robust convergence result in the face of bounded noise. This robustness property distinguishes the algorithm from existing linear schemes. A key idea of

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