A hardware implementation of an analog neural network for Gaussian peak-fitting

Tommy Akkila, Thomas Lindblad, Bengt Lund-Jensen, Geza Szekely, Åge Eide

Research output: Contribution to journalArticle

6 Citations (Scopus)


This paper demonstrates the implementation in hardware of an electrically trainable analog neural network (ETANN) for finding the position and width (FWHM) of an ion-beam hitting a strip-detector. This is accomplished using a single ETANN chip with 32 neurons in one hidden layer. The network finds the maximum and the FWHM, with an error of 0.1 and 0.2, respectively, of the 16 wire input pitch. An extension of this linear peak-fitting problem to include finding the height is presented. Extensions to larger nets with 64 and 128 inputs are presented as multi-chip solutions. A track-finding problem using several chips is briefly discussed.

Original languageEnglish
Pages (from-to)573-579
Number of pages7
JournalNuclear Inst. and Methods in Physics Research, A
Issue number2-3
Publication statusPublished - Apr 1 1993


ASJC Scopus subject areas

  • Nuclear and High Energy Physics
  • Instrumentation

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