Extracting model parameters from experimental data using the minimum information principle

1 April 2000

journal article
Published by Pleiades Publishing Ltd in Technical Physics

Vol. 45 (4) , 512-514
https://doi.org/10.1134/1.1259667

Abstract

A method is described for extracting model parameters of a physical process from statistical experimental data based on the minimum information principle. Potentialities of the method are studied by examples of regular and chaotic processes simulated by the transport and diffusion equation and by the logistic mapping.

Keywords

STATISTICAL
PROCESSES SIMULATED
INFORMATION PRINCIPLE
MINIMUM INFORMATION
DIFFUSION
EXTRACTING MODEL PARAMETERS
CHAOTIC PROCESSES

This publication has 3 references indexed in Scilit:

Inverting chaos: Extracting system parameters from experimental data
Chaos: An Interdisciplinary Journal of Nonlinear Science, 1996
Information and Self-Organization
Published by Springer Nature ,1988
The use of mathematical-statistics methods in the solution of incorrectly posed problems
Uspekhi Fizicheskih Nauk, 1970