Information Closure Method for Dynamic Analysis of Nonlinear Stochastic Systems-Reference-Cited by-同舟云学术

Information Closure Method for Dynamic Analysis of Nonlinear Stochastic Systems

Published:2002-07-23 Issue:3 Volume:124 Page:353-363
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Chang R. J.¹,Lin S. J.¹

Affiliation:

1. Department of Mechanical Engineering, National Cheng Kung University, 701 Tainan, Taiwan, R.O.C.

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.

Publisher

ASME International

Subject

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

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/124/3/353/5779462/353_1.pdf

Reference21 articles.

1. Maybeck, P. S., 1982, Stochastic Models, Estimation, and Control, Vol. 2, Academic Press, New York.

2. Sobczyk, K., 1991, Stochastic Differential Equations with Applications to Physics and Engineering, Kluwer Academic Publishers, Boston.

3. Chang, R. J. , 1993, “Extension in Techniques for Stochastic Dynamic Systems,” Control and Dynamic Systems, 55, pp. 429–470.

4. Dashevskii, M. L., and Liptser, R. S., 1967, “Application of Conditional Semi-Invariants in Problems of Non-Linear Filtering of Markov Process,” Autom. Remote Control (Engl. Transl.), 28, pp. 912–921.

5. Haken, H., 1988, Information and Self-Organization, Springer-Verlag, Berlin.

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Identifying explicit expression of response probability density of nonlinear stochastic system: Information-theoretic method;Mechanical Systems and Signal Processing;2020-09

2. Extension of Nonlinear Stochastic Solution to Include Sinusoidal Excitation—Illustrated by Duffing Oscillator;Journal of Computational and Nonlinear Dynamics;2017-07-12

3. Two-stage optimal stochastic linearization in analyzing of non-linear stochastic dynamic systems;International Journal of Non-Linear Mechanics;2014-01

4. Statistical Linearization Model for the Response Prediction of Nonlinear Stochastic Systems Through Information Closure Method;Journal of Vibration and Acoustics;2004-07-01