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Particle filtering based identification for autonomous nonlinear ODE models

Nordh, Jerker LU ; Wigren, Torbjörn; B. Schön, Thomas and Bernhardsson, Bo LU (2015) In IFAC-PapersOnLine 48(28). p.415-420
Abstract

This paper presents a new black-box algorithm for identification of a nonlinear autonomous system in stable periodic motion. The particle filtering based algorithm models the signal as the output of a continuous-time second order ordinary differential equation (ODE). The model is selected based on previous work which proves that a second order ODE is sufficient to model a wide class of nonlinear systems with periodic modes of motion, also systems that are described by higher order ODEs. Such systems are common in systems biology. The proposed algorithm is applied to data from the well-known Hodgkin-Huxley neuron model. This is a challenging problem since the Hodgkin-Huxley model is a fourth order model, but has a mode of oscillation in... (More)

This paper presents a new black-box algorithm for identification of a nonlinear autonomous system in stable periodic motion. The particle filtering based algorithm models the signal as the output of a continuous-time second order ordinary differential equation (ODE). The model is selected based on previous work which proves that a second order ODE is sufficient to model a wide class of nonlinear systems with periodic modes of motion, also systems that are described by higher order ODEs. Such systems are common in systems biology. The proposed algorithm is applied to data from the well-known Hodgkin-Huxley neuron model. This is a challenging problem since the Hodgkin-Huxley model is a fourth order model, but has a mode of oscillation in a second order subspace. The numerical experiments show that the proposed algorithm does indeed solve the problem.

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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Autonomous system, identification, neural dynamics, nonlinear systems, oscillation, particle filtering, periodic system, phase plane
in
IFAC-PapersOnLine
volume
48
issue
28
pages
6 pages
publisher
IFAC Secretariat
external identifiers
  • scopus:84988474876
DOI
10.1016/j.ifacol.2015.12.163
language
English
LU publication?
yes
id
134cdb4c-2a3f-44e3-8355-6ca47f544f9d
date added to LUP
2017-02-23 09:17:41
date last changed
2017-09-24 05:07:49
@article{134cdb4c-2a3f-44e3-8355-6ca47f544f9d,
  abstract     = {<p>This paper presents a new black-box algorithm for identification of a nonlinear autonomous system in stable periodic motion. The particle filtering based algorithm models the signal as the output of a continuous-time second order ordinary differential equation (ODE). The model is selected based on previous work which proves that a second order ODE is sufficient to model a wide class of nonlinear systems with periodic modes of motion, also systems that are described by higher order ODEs. Such systems are common in systems biology. The proposed algorithm is applied to data from the well-known Hodgkin-Huxley neuron model. This is a challenging problem since the Hodgkin-Huxley model is a fourth order model, but has a mode of oscillation in a second order subspace. The numerical experiments show that the proposed algorithm does indeed solve the problem.</p>},
  author       = {Nordh, Jerker and Wigren, Torbjörn and B. Schön, Thomas and Bernhardsson, Bo},
  keyword      = {Autonomous system,identification,neural dynamics,nonlinear systems,oscillation,particle filtering,periodic system,phase plane},
  language     = {eng},
  number       = {28},
  pages        = {415--420},
  publisher    = {IFAC Secretariat},
  series       = {IFAC-PapersOnLine},
  title        = {Particle filtering based identification for autonomous nonlinear ODE models},
  url          = {http://dx.doi.org/10.1016/j.ifacol.2015.12.163},
  volume       = {48},
  year         = {2015},
}