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

Robust nonlinear system identification using neural-network models.

S Lu1, T Basar

  • 1Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801, USA.

IEEE Transactions on Neural Networks
|February 7, 2008
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

Cyclodextrin type dependent host-guest interaction mode with phthalocyanine and their influence on photodynamic activity to cancer.

Carbohydrate polymers·2016
Same author

New allies fight for China's environment.

Science (New York, N.Y.)·2016
Same author

Targeted sequencing of maternal plasma for haplotype-based non-invasive prenatal testing of spinal muscular atrophy.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology·2016
Same author

A role for FOXO1 in BCR-ABL1-independent tyrosine kinase inhibitor resistance in chronic myeloid leukemia.

Leukemia·2016
Same author

Lymph node involvement influenced by lung adenocarcinoma subtypes in tumor size ≤3 cm disease: A study of 2268 cases.

European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology·2016
Same author

Adiponectin Promotes Human Jaw Bone Marrow Stem Cell Osteogenesis.

Journal of dental research·2016
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces novel algorithms for identifying nonlinear systems with noise, overcoming standard excitation condition challenges. The methods successfully identify system nonlinearities using neural networks, even with noisy measurements.

Area of Science:

  • Control Systems Engineering
  • Machine Learning
  • Nonlinear System Identification

Background:

  • Standard neural identification methods face challenges with persistency of excitation conditions.
  • Unknown driving noise complicates nonlinear system identification.
  • Existing techniques often require specific input signal properties.

Purpose of the Study:

  • To develop robust identification algorithms for nonlinear systems with unknown driving noise.
  • To circumvent the persistency of excitation condition limitations in neural identification.
  • To enable accurate nonlinearity identification using neural network models.

Main Methods:

  • Utilized feedforward multilayer neural networks and radial basis function networks.
  • Employed a novel formulation and new identification algorithms by Didinsky et al.

Related Experiment Videos

  • Developed L1, L2, and L-infinity cost criteria-based identifiers, including an H-infinity approach.
  • Incorporated global optimization techniques and modified existing learning algorithms like backpropagation and genetic algorithms.

    • Main Results:

    • Successfully identified system nonlinearities using neural network models.
    • Achieved good approximation of system nonlinearity with noise-perturbed state measurements.
    • Demonstrated robust identification under L-infinity criterion using an H-infinity based algorithm.
    • Simulation studies confirmed the effectiveness of the proposed identification algorithms.

    Conclusions:

    • The novel algorithms effectively address the identification of nonlinear systems in the presence of noise.
    • The proposed methods overcome limitations related to persistency of excitation conditions.
    • Neural network models, combined with advanced algorithms, provide a powerful tool for robust system identification.