Curtin University Homepage
  • Library
  • Help
    • Admin

    espace - Curtin’s institutional repository

    JavaScript is disabled for your browser. Some features of this site may not work without it.
    View Item 
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item
    • espace Home
    • espace
    • Curtin Research Publications
    • View Item

    A gradient-based parameter identification method for time-delay chaotic systems

    199977_199977.pdf (233.5Kb)
    Access Status
    Open access
    Authors
    Chai, Q.
    Loxton, Ryan
    Date
    2014
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Chai, Q. and Loxton, R. 2014. A gradient-based parameter identification method for time-delay chaotic systems, in Xu, S. and Zhao, Q. (ed), Proceedings of the 33rd Chinese Control Conference, Jul 28-30 2014, pp. 6566-6570. Nanjing, China: IEEE.
    Source Title
    Proceedings of the 33rd Chinese Control Conference
    Source Conference
    33rd Chinese Control Conference
    School
    Department of Mathematics and Statistics
    Remarks

    Copyright © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/7227
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, the parameter identification problem for a general class of time-delay chaotic systems is considered. The objective of the problem is to determine optimal values for an unknown time-delay and unknown system parameters such that the dynamic model of the system best fits given experimental data. We propose a gradient-based optimization algorithm to solve this problem, where accurate values for the partial derivatives of the error function are obtained by solving a set of auxiliary time-delay systems. Simulation results for two example problems show that the proposed algorithm is robust and efficient.

    Related items

    Showing items related by title, author, creator and subject.

    • Computational methods for solving optimal industrial process control problems
      Chai, Qinqin (2013)
      In this thesis, we develop new computational methods for three classes of dynamic optimization problems: (i) A parameter identification problem for a general nonlinear time-delay system; (ii) an optimal control problem ...
    • Robust optimization for nonlinear time-delay dynamical system of dha regulon with cost sensitivity constraint in batch culture
      Yuan, J.; Zhang, X.; Liu, Chongyang; Chang, L.; Xie, J.; Feng, E.; Yin, H.; Xiu, Z. (2016)
      Time-delay dynamical systems, which depend on both the current state of the system and the state at delayed times, have been an active area of research in many real-world applications. In this paper, we consider a nonlinear ...
    • Parameter estimation for nonlinear time-delay systems with noisy output measurements
      Lin, Qun; Loxton, Ryan; Xu, C.; Teo, Kok Lay (2015)
      This paper considers the problem of using noisy output data to estimate unknown time-delays and unknown system parameters in a general nonlinear time-delay system. We formulate the problem as a dynamic optimization problem ...
    Advanced search

    Browse

    Communities & CollectionsIssue DateAuthorTitleSubjectDocument TypeThis CollectionIssue DateAuthorTitleSubjectDocument Type

    My Account

    Admin

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Follow Curtin

    • 
    • 
    • 
    • 
    • 

    CRICOS Provider Code: 00301JABN: 99 143 842 569TEQSA: PRV12158

    Copyright | Disclaimer | Privacy statement | Accessibility

    Curtin would like to pay respect to the Aboriginal and Torres Strait Islander members of our community by acknowledging the traditional owners of the land on which the Perth campus is located, the Whadjuk people of the Nyungar Nation; and on our Kalgoorlie campus, the Wongutha people of the North-Eastern Goldfields.