DocumentCode
2977149
Title
Approximate stochastic models
Author
Gombani, Andrea
Author_Institution
LADSEB-CNR, Padova, Italy
fYear
1988
fDate
7-9 Dec 1988
Firstpage
1928
Abstract
The problem of representing a stationary stochastic process y with a low-dimensional stochastic model is considered. This problem occurs when the state space of an exact realization of y has very large dimension. The reduction is obtained in this large state space, exploiting its Markovian structure to characterize all Markovian subspaces, among which a reduced k -dimensional model is sought. An algorithm with polynomial complexity to compute the approximate model is given
Keywords
Markov processes; computational complexity; modelling; stochastic systems; Markovian structure; Markovian subspaces; approximate stochastic models; high-dimensional state space; low-dimensional model; polynomial complexity; stationary stochastic process; History; Markov processes; Observability; Polynomials; Reduced order systems; State-space methods; Stochastic processes; Stochastic resonance; Vectors; White noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1988., Proceedings of the 27th IEEE Conference on
Conference_Location
Austin, TX
Type
conf
DOI
10.1109/CDC.1988.194666
Filename
194666
Link To Document