• DocumentCode
    1160104
  • Title

    Associative recall using a contraction operator

  • Author

    Stubberud, Allen R. ; Thomas, Robert J.

  • Author_Institution
    Dept. of Electr. Eng., California Univ., Irvine, CA, USA
  • Volume
    36
  • Issue
    5
  • fYear
    1989
  • fDate
    5/1/1989 12:00:00 AM
  • Firstpage
    682
  • Lastpage
    686
  • Abstract
    An associative memory can be defined as a transformation between two sets. Under mild conditions, the associative recall problem can be formulated as that of solving an equation of the form y=f (x), where y is known and the corresponding value x is not. Here, the associative recall problem is formulated in this way, and conditions on f are developed such that a contraction operator can be developed which solves the given equation. A specific piecewise-linear function is then chosen, and its associative recall properties are discussed. This associative memory is shown to converge rapidly and to have noise rejection properties and some learning capability
  • Keywords
    content-addressable storage; neural nets; associative memory; associative recall problem; contraction operator; converge rapidly; learning capability; noise rejection properties; piecewise-linear function; transformation between two sets; Associative memory; Convergence; Discrete transforms; Equations; Mathematics; Neural networks; Piecewise linear techniques; Problem-solving;
  • fLanguage
    English
  • Journal_Title
    Circuits and Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0098-4094
  • Type

    jour

  • DOI
    10.1109/31.31316
  • Filename
    31316