Subspace Methods for System Identification
Publication year: 2005
ISBN: 978-1-84628-158-7
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the mathematical preliminaries are dealt with: numerical linear algebra; system theory; stochastic processes; and Kalman filtering. The second part explains realization theory, particularly that based on the decomposition of Hankel matrices, as it is applied to subspace identification methods. Two stochastic realization results are included, one based on spectral factorization and Riccati equations, the other on canonical correlation analysis (CCA) for stationary processes. Part III uses the development of stochastic realization results, in the presence of exogenous inputs, to demonstrate the closed-loop application of subspace identification methods CCA and ORT (based on orthogonal decomposition).
Subject: Engineering, Control, Control Engineering, Control Systems, Control Theory, Filter, Kalman Filtering, Kalman filter, Linear Systems, Process Control, Realization Theory, Subspace, development, filtering, signal processing, systems theory
