Publication detail

Simulation of Multiconductor Transmission Lines with Random Parameters via Stochastic Differential Equations Approach.

BRANČÍK, L. KOLÁŘOVÁ, E.

Original Title

Simulation of Multiconductor Transmission Lines with Random Parameters via Stochastic Differential Equations Approach.

Type

journal article in Web of Science

Language

English

Original Abstract

This article addresses a method for the simulation of multiconductor transmission lines (MTLs) with fluctuating parameters based on the theory of stochastic differential equations (SDEs). Specifically, confidence intervals of an MTL models stochastic responses are effectively evaluated. First, the MTLs deterministic model with lumped parameters, based on generalized PI sections connected in cascade, is formulated and described through a state variable method, which results in a vector ordinary differential equation (ODE) in the time domain. A vector SDE is then developed by incorporating the respective stochastic processes into its deterministic counterpart. Next, the first two moments of the stochastic processes are calculated via the solution of respective Lyapunov-like ODEs, to assess expectations and the variances of stochastic responses, and also to determine relevant confidence intervals. A statistical processing of individual stochastic trajectories is used to validate the results.

Keywords

multiconductor transmission line; random parameter; stochastic differential equation; variance; confidence interval; MATLAB

Authors

BRANČÍK, L.; KOLÁŘOVÁ, E.

Released

1. 6. 2016

Publisher

SAGE Publishing

Location

London, United Kingdom

ISBN

0037-5497

Periodical

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL

Year of study

92

Number

6

State

United Kingdom of Great Britain and Northern Ireland

Pages from

521

Pages to

533

Pages count

13

URL

BibTex

@article{BUT125050,
  author="Lubomír {Brančík} and Edita {Kolářová}",
  title="Simulation of Multiconductor Transmission Lines with Random Parameters via Stochastic Differential Equations Approach.",
  journal="SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL",
  year="2016",
  volume="92",
  number="6",
  pages="521--533",
  doi="10.1177/0037549716645198",
  issn="0037-5497",
  url="http://sim.sagepub.com/content/92/6/521.full.pdf?ijkey=0hH1aBawL74zJaX&keytype=finite"
}