APPROXIMATION OF MARKOVIAN MODELS WITH NON-CONSTANT PARAMETERS.

Uday B. Desai; Saibal Banerjee; Sayfollah Kiaei

doi:10.1109/cdc.1984.272381

APPROXIMATION OF MARKOVIAN MODELS WITH NON-CONSTANT PARAMETERS.

Uday B. Desai, Saibal Banerjee, Sayfollah Kiaei

Research output: Contribution to journal › Conference article › peer-review

Abstract

A generalization of the canonical correlation analysis approach has been developed for nonstationary processes generated by Markovian models with nonconstant parameters. This generalization is then used to develop two model reduction (approximation) algorithms.

Original language	English (US)
Pages (from-to)	1642-1644
Number of pages	3
Journal	Proceedings of the IEEE Conference on Decision and Control
DOIs	https://doi.org/10.1109/cdc.1984.272381
State	Published - Jan 1 1984
Externally published	Yes

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

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10.1109/cdc.1984.272381

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title = "APPROXIMATION OF MARKOVIAN MODELS WITH NON-CONSTANT PARAMETERS.",

abstract = "A generalization of the canonical correlation analysis approach has been developed for nonstationary processes generated by Markovian models with nonconstant parameters. This generalization is then used to develop two model reduction (approximation) algorithms.",

author = "Desai, {Uday B.} and Saibal Banerjee and Sayfollah Kiaei",

year = "1984",

month = jan,

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doi = "10.1109/cdc.1984.272381",

language = "English (US)",

pages = "1642--1644",

journal = "Proceedings of the IEEE Conference on Decision and Control",

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AU - Desai, Uday B.

AU - Banerjee, Saibal

AU - Kiaei, Sayfollah

PY - 1984/1/1

Y1 - 1984/1/1

N2 - A generalization of the canonical correlation analysis approach has been developed for nonstationary processes generated by Markovian models with nonconstant parameters. This generalization is then used to develop two model reduction (approximation) algorithms.

AB - A generalization of the canonical correlation analysis approach has been developed for nonstationary processes generated by Markovian models with nonconstant parameters. This generalization is then used to develop two model reduction (approximation) algorithms.

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DO - 10.1109/cdc.1984.272381

M3 - Conference article

AN - SCOPUS:0021642161

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JO - Proceedings of the IEEE Conference on Decision and Control

JF - Proceedings of the IEEE Conference on Decision and Control

ER -

APPROXIMATION OF MARKOVIAN MODELS WITH NON-CONSTANT PARAMETERS.

Abstract

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