Title :
Approximate stochastic models
Author_Institution :
LADSEB-CNR, Padova, Italy
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;
Conference_Titel :
Decision and Control, 1988., Proceedings of the 27th IEEE Conference on
Conference_Location :
Austin, TX
DOI :
10.1109/CDC.1988.194666
