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- W52203363 abstract "Publisher Summary Recently, a fairly simple state-space system identification algorithm has been introduced, which uses I/O data directly. This new algorithm falls in the class of subspace methods and resembles a well known Markov parameter based realization algorithm. The heart of the subspace algorithm is the computation of a state vector sequence from the intersection of two subspaces. Once this state vector sequence is obtained, the system matrices can be easily obtained by solving an over-determined linear system of equations. It has been shown that this new subspace algorithm is equivalent to a canonical correlation analysis on certain data matrices. This chapter applies the classical theory of canonical correlation analysis to the deterministic identification problem. It is shown that the first n pairs of canonical eigenvectors contain all the information necessary to compute the state vector sequence in the original algorithm. The chapter shows the connections with forward-backwards models, a duality property common to canonical correlation based identification algorithms. This is useful for computing balanced realizations (an optional step). It is shown that, unlike the stochastic realization problem, certain dual covariance constraints have to be satisfied in order for the state sequence to be admissible thereby forcing the optimal solution to be of a canonical correlation analysis type. Algorithms that do not satisfy all the constraints are only approximate. The chapter concludes with derivation of deterministic realization algorithms using ideas from their stochastic realization counterparts." @default.
- W52203363 created "2016-06-24" @default.
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- W52203363 date "1995-01-01" @default.
- W52203363 modified "2023-09-22" @default.
- W52203363 title "Canonical correlation analysis of the deterministic realization problem" @default.
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- W52203363 doi "https://doi.org/10.1016/b978-044482107-2/50046-7" @default.
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