Pattern recognition in high energy physics with artificial neural networks - JETNET 2.0

Summary, in English

A F77 package of adaptive artificial neural network algorithms, JETNET 2.0, is presented. Its primary target is the high energy physics community, but it is general enough to be used in any pattern-recognition application area. The basic ingredients are the multilayer perceptron back-propagation algorithm and the topological self-organizing map. The package consists of a set of subroutines, which can either be used with standard options or be easily modified to host alternative architectures and procedures.

• Författare: Leif Lönnblad; Carsten Peterson; Thorsteinn Rögnvalsson
• Avdelning/ar: Department of Astronomy and Theoretical Physics
• Publiceringsår: 1992-05
• Språk: Engelska
• Sidor: 167-182
• Publikation/Tidskrift/Serie: Computer Physics Communications
• Volym: 70
• Issue: 1
• Länkar: http://dx.doi.org/10.1016/0010-4655(92)90099-K
• Dokumenttyp: Artikel i tidskrift
• Förlag: Elsevier
• Ämne: Subatomic Physics
• Aktiv: Published
• ISBN/ISSN/Övrigt: ISSN: 0010-4655