Information closure method for dynamic analysis of nonlinear stochastic systems

Ren-Jung Chang, S. J. Lin

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

An information closure method for analytical investigation of response statistics and robust stability of nonlinear stochastic dynamic systems is proposed. Entropy modes are defined first based on the decomposition of probability density functions estimated by maximizing entropy in quasi-stationary. Then the entropy modes are selected and employed in the moment equations as the constraints for information closure. The estimated density with Lagrange multipliers is used for the closure of the hierarchical moment equations. By selecting single independent mode in every state, an explicit analysis of the entropy and density function can be obtained. The performance of the closure method is supported by employing three stochastic systems with some stationary exact solutions and through Monte Carlo simulations.

Original languageEnglish
Pages (from-to)353-363
Number of pages11
JournalJournal of Dynamic Systems, Measurement and Control, Transactions of the ASME
Volume124
Issue number3
DOIs
Publication statusPublished - 2002 Sep 1

Fingerprint

Stochastic systems
Dynamic analysis
closures
Entropy
entropy
Probability density function
moments
Lagrange multipliers
probability density functions
Dynamical systems
Statistics
statistics
Decomposition
decomposition
simulation

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Instrumentation
  • Mechanical Engineering
  • Computer Science Applications

Cite this

@article{d74af772fd8a454b8182fd4cae213bec,
title = "Information closure method for dynamic analysis of nonlinear stochastic systems",
abstract = "An information closure method for analytical investigation of response statistics and robust stability of nonlinear stochastic dynamic systems is proposed. Entropy modes are defined first based on the decomposition of probability density functions estimated by maximizing entropy in quasi-stationary. Then the entropy modes are selected and employed in the moment equations as the constraints for information closure. The estimated density with Lagrange multipliers is used for the closure of the hierarchical moment equations. By selecting single independent mode in every state, an explicit analysis of the entropy and density function can be obtained. The performance of the closure method is supported by employing three stochastic systems with some stationary exact solutions and through Monte Carlo simulations.",
author = "Ren-Jung Chang and Lin, {S. J.}",
year = "2002",
month = "9",
day = "1",
doi = "10.1115/1.1485746",
language = "English",
volume = "124",
pages = "353--363",
journal = "Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME",
issn = "0022-0434",
publisher = "American Society of Mechanical Engineers(ASME)",
number = "3",

}

TY - JOUR

T1 - Information closure method for dynamic analysis of nonlinear stochastic systems

AU - Chang, Ren-Jung

AU - Lin, S. J.

PY - 2002/9/1

Y1 - 2002/9/1

N2 - An information closure method for analytical investigation of response statistics and robust stability of nonlinear stochastic dynamic systems is proposed. Entropy modes are defined first based on the decomposition of probability density functions estimated by maximizing entropy in quasi-stationary. Then the entropy modes are selected and employed in the moment equations as the constraints for information closure. The estimated density with Lagrange multipliers is used for the closure of the hierarchical moment equations. By selecting single independent mode in every state, an explicit analysis of the entropy and density function can be obtained. The performance of the closure method is supported by employing three stochastic systems with some stationary exact solutions and through Monte Carlo simulations.

AB - An information closure method for analytical investigation of response statistics and robust stability of nonlinear stochastic dynamic systems is proposed. Entropy modes are defined first based on the decomposition of probability density functions estimated by maximizing entropy in quasi-stationary. Then the entropy modes are selected and employed in the moment equations as the constraints for information closure. The estimated density with Lagrange multipliers is used for the closure of the hierarchical moment equations. By selecting single independent mode in every state, an explicit analysis of the entropy and density function can be obtained. The performance of the closure method is supported by employing three stochastic systems with some stationary exact solutions and through Monte Carlo simulations.

UR - http://www.scopus.com/inward/record.url?scp=4444372621&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=4444372621&partnerID=8YFLogxK

U2 - 10.1115/1.1485746

DO - 10.1115/1.1485746

M3 - Article

VL - 124

SP - 353

EP - 363

JO - Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

JF - Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME

SN - 0022-0434

IS - 3

ER -