Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Closed-loop nonlinear system identification via the vector optimal parameter search algorithm: application to heart

Hengliang Wang1, Kihwan Ju, Ki H Chon

  • 1Department of Biomedical Engineering, State University of New York at Stony Brook (SUNY@ Stony Brook), HSC T18, Rm. 030, Stony Brook, NY 11794-8181, United States.

Medical Engineering & Physics
|August 22, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cascade Skip-Connection BiLSTM Autoencoder for CPR Artifact Removal Prior to AED Shock Advisory.

IEEE open journal of the Computer Society·2026
Same author

ospEDA: Orthogonal Subspace Projection for Electrodermal Activity Decomposition.

IEEE transactions on bio-medical engineering·2026
Same author

Multimodal Detection of Pain and Anticipation Anxiety from Ultra-Short Duration Wearable Sensors Measurements.

Sensors (Basel, Switzerland)·2026
Same author

Host-independent metagenomics reveal gut bacteria contribution to Delia antiqua growth by vitamin B6 provision.

Insect molecular biology·2026
Same author

Modular Development of a <i>Klebsiella pneumoniae</i> Bioconjugate Nanovaccine Elicits Robust Protection via Intranasal Immunization.

Nanomaterials (Basel, Switzerland)·2026
Same author

A tonic electrocutaneous stimulation paradigm for graded sensory C-fiber engagement in healthy subjects.

Frontiers in pain research (Lausanne, Switzerland)·2026
Same journal

Enabling laboratory-based personalization of musculoskeletal spine models: a standardized rail-guided ultrasound method.

Medical engineering & physics·2026
Same journal

From diagnostic labels to radiology reports: a unified multi-modal framework for lesion detection and segmentation.

Medical engineering & physics·2026
Same journal

Dual-view cross-semantic graph neural network for predicting intraoperative complications in patients with acute myocardial infarction undergoing percutaneous coronary intervention.

Medical engineering & physics·2026
Same journal

Finite element analysis explores ablation depth and optical zone as key determinants of intraocular pressure reduction after refractive surgery.

Medical engineering & physics·2026
Same journal

A preliminary technique development for quantitative analysis of transhumeral bone, post-amputation, using a computed tomography BMD phantom.

Medical engineering & physics·2026
Same journal

Characteristics and performance evaluation of the Maastricht room calorimeter systems.

Medical engineering & physics·2026
See all related articles

New algorithms, vector optimal parameter search (VOPS) and constrained optimal parameter search (COPS), enhance nonlinear system identification. These methods offer superior parameter estimates compared to traditional approaches like vector least squares (VLS).

Area of Science:

  • Systems Biology
  • Control Theory
  • Signal Processing

Background:

  • Existing algorithms like vector least squares (VLS) are limited in identifying complex closed-loop nonlinear systems.
  • The vector optimal parameter search (VOPS) and constrained optimal parameter search (COPS) were recently developed for linear systems.
  • Accurate parameter estimation is crucial for understanding and controlling dynamic physiological systems.

Purpose of the Study:

  • To extend the VOPS and COPS algorithms for application to closed-loop nonlinear system identification.
  • To compare the performance of extended VOPS and COPS against VLS and total least squares (TLS) using Monte Carlo simulations.
  • To evaluate the clinical applicability of the extended algorithms in a physiological context, specifically the heart rate baroreflex.

Main Methods:

Related Experiment Videos

  • Development of extended VOPS and COPS algorithms for vector nonlinear autoregressive models.
  • Monte Carlo simulations of nonlinear closed-loop systems to assess parameter estimation accuracy.
  • Calculation of relative error and linear transfer functions for performance comparison.
  • Application of extended methods to the human heart rate baroreflex system under different physiological conditions.

Main Results:

  • Extended VOPS and COPS significantly outperform VLS in parameter estimation for nonlinear closed-loop systems.
  • Total least squares (TLS) performs well with observation noise but degrades substantially with dynamic noise.
  • The heart rate baroreflex exhibits predominantly linear dynamics during control but shows increased nonlinearity during parasympathetic blockade.
  • Mutual information analysis statistically supports the observed differences in nonlinearity.

Conclusions:

  • The extended VOPS and COPS algorithms represent a significant advancement for identifying closed-loop nonlinear systems.
  • These methods offer improved accuracy over traditional VLS, particularly in the presence of dynamic noise.
  • The study demonstrates the utility of VOPS and COPS in analyzing physiological systems, revealing condition-dependent nonlinear dynamics in the heart rate baroreflex.