A Modification of the Lernmatrix for Real Valued Data Processing

  • José Juan Carbajal-Hernández
  • Luis P. Sánchez-Fernández
  • Luis A. Sánchez-Pérez
  • Jesús Ariel Carrasco-Ochoa
  • José Francisco Martínez-Trinidad
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)

Abstract

An associative memory is a binary relationship between inputs and outputs, which is stored in an M matrix. In this paper, we propose a modification of the Steinbuch Lernmatrix model in order to process real-valued patterns, avoiding binarization processes and reducing computational burden. The proposed model is used in experiments with noisy environments, where the performance and efficiency of the memory is proven. A comparison between the proposed and the original model shows a good response and efficiency in the classification process of the new Lernmatrix.

Keywords

Associative memories artificial intelligence neurocomputing pattern processing classifier 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • José Juan Carbajal-Hernández
    • 1
    • 2
  • Luis P. Sánchez-Fernández
    • 2
  • Luis A. Sánchez-Pérez
    • 2
  • Jesús Ariel Carrasco-Ochoa
    • 1
  • José Francisco Martínez-Trinidad
    • 1
  1. 1.Computer Science DepartmentNational Institute of Astrophysics, Optics and ElectronicsMéxico
  2. 2.Center of Computer ResearchNational Polytechnic InstituteMéxico D.F.México

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