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Dipolar Designing Layers of Formal Neurons

  • Leon Bobrowski
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 311)

Abstract

A layer of formal neurons can perform separable data aggregation. The term (separable data aggregation( means that a number of input vectors belonging to one category (class) are merged by the layer in one output vector with an additional condition that input vectors belonging to different categories are not aggregated. Dipolar principles of separable layers designing are examined in the paper. Hierarchical networks can be designed from separable layers and used for aggregation of all input vectors belonging to one category in an output vector.

Keywords

separable learning sets separable aggregation formal neurons dipolar layers 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leon Bobrowski
    • 1
    • 2
  1. 1.Faculty of Computer ScienceBiałystok Technical UniversityPoland
  2. 2.Institute of Biocybernetics and Biomedical EngineeringPASWarsawPoland

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