• DocumentCode
    1161611
  • Title

    Approximation capabilities of hierarchical hybrid systems

  • Author

    Zeng, Xiao-Jun ; Keane, John A.

  • Author_Institution
    Sch. of Informatics, Univ. of Manchester
  • Volume
    36
  • Issue
    5
  • fYear
    2006
  • Firstpage
    1029
  • Lastpage
    1039
  • Abstract
    This paper investigates the approximation capabilities of hierarchical hybrid systems, which are motivated by research in hierarchical fuzzy systems, hybrid intelligent systems, and modeling of model partly known systems. For a function (system) with known hierarchical structure (i.e., one that can be represented as a composition of some simpler and lower dimensional subsystems), it is shown that hierarchical hybrid systems have the structure approximation capability in the sense that such a hybrid approximation scheme can approximate both the overall system and all the subsystems to any desired degree of accuracy. For a function (system) with unknown hierarchical structure, Kolmogorov´s theorem is used to construct the hierarchical structure of the given function (system). It is then shown that hierarchical hybrid systems are universal approximators
  • Keywords
    function approximation; fuzzy systems; hierarchical systems; neural nets; approximation capabilities; function system; hierarchical fuzzy systems; hierarchical hybrid system; hybrid intelligent systems; Approximation methods; Fuzzy systems; Hierarchical systems; Hybrid intelligent systems; Intelligent networks; Mathematical model; Neural networks; Pattern recognition; Power system modeling; Predictive models; Fuzzy systems; hierarchical systems; hybrid intelligent systems; neural networks; universal approximation;
  • fLanguage
    English
  • Journal_Title
    Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1083-4427
  • Type

    jour

  • DOI
    10.1109/TSMCA.2006.878972
  • Filename
    1678030