• DocumentCode
    296039
  • Title

    Performance improvements for a neural network detector

  • Author

    Andina, Diego ; Sanz-GonzÁlez, José L. ; Rodríguez-Martin, Octavio A.

  • Author_Institution
    ETSI Telecomunicacion, Univ. Politecnica de Madrid, Spain
  • Volume
    1
  • fYear
    1995
  • fDate
    Nov/Dec 1995
  • Firstpage
    492
  • Abstract
    In this paper, a neural detector is purposed. It can be applied to binary detection problems such as those found in radar or sonar. Topics about designing the structure, training procedure and evaluating the performance, are discussed. The detector optimization is based on the use of a criterion function that yields a solution significantly superior to the typical sum-of-square-error. Using a modeled input, its performance is evaluated by Monte Carlo trials. As a result, receiver operating characteristics and detection curves are presented
  • Keywords
    Monte Carlo methods; acoustic signal detection; backpropagation; multilayer perceptrons; probability; radar detection; Monte Carlo trials; binary detection problems; detection curves; neural network detector; performance evaluation; radar; receiver operating characteristics; sonar; sum-of-square-error; training procedure; Detectors; Envelope detectors; Least squares approximation; Monte Carlo methods; Neural networks; Optical noise; Radar detection; Robustness; Sonar applications; Sonar detection; Telecommunication standards; Very large scale integration;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1995. Proceedings., IEEE International Conference on
  • Conference_Location
    Perth, WA
  • Print_ISBN
    0-7803-2768-3
  • Type

    conf

  • DOI
    10.1109/ICNN.1995.488226
  • Filename
    488226