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Unsupervised Learning Neural Networks

  • Patricia Melin
  • Oscar Castillo
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 172)

Abstract

This chapter introduces the basic concepts and notation of unsupervised learning neural networks. Unsupervised networks are useful for analyzing data without having the desired outputs; in this case, the neural networks evolve to capture density characteristics of a data phase. We will describe in some detail competitive learning networks, Kohonen self-organizing networks, learning vector quantization, and Hopfield networks. We will also show some examples of these networks to illustrate their possible application in solving real-world problems in pattern recognition.

Keywords

Weight Vector Cluster Center Linguistic Term Output Unit Learn Vector Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors and Affiliations

  • Patricia Melin
    • 1
  • Oscar Castillo
    • 2
  1. 1.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA
  2. 2.Department of Computer ScienceTijuana Institute of TechnologyChula VistaUSA

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