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A transformation approach to stochastic model reduction

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2 Author(s)
U. Desai ; Washington State University, Pullman, WA, USA ; D. Pal

A new and direct approach to stochastic model reduction is developed. The order reduction algorithm is obtained by establishing an equivalence between canonical correlation analysis and solutions to algebraic Riccati equations. Also the concept of balanced stochastic realization (BSR) plays a fundamental role. Asymptotic stability of the reduced-order realization is established, and spectral domain interpretations for the BSR are given.

Published in:

IEEE Transactions on Automatic Control  (Volume:29 ,  Issue: 12 )