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Flexible Neural Networks

  • Mohammad Teshnehlab
  • Keigo Watanabe
Chapter
Part of the International Series on Microprocessor-Based and Intelligent Systems Engineering book series (ISCA, volume 19)

Abstract

The application of ANNs has been a subject of extensive studies in the past four decades. There are several types of NNs that can be used in control systems as discussed in Chapter 2: the multi-layered feedforward, the Kohonen’s self-organizing map [1], the Hopfield network [2] and the Boltzmann machine [3], etc. These types of NNs are based on biological nervous system. The layered structure of parts of the brain, and multilayer (instead of single layer) arrangement of neurons in biological systems comprise the main idea of mimicking the biological neural system for obtaining higher capabilities in learning algorithms.

Keywords

Sigmoid Function Connection Weight Boltzmann Machine Teaching Signal Hopfield Network 
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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Copyright information

© Springer Science+Business Media Dordrecht 1999

Authors and Affiliations

  • Mohammad Teshnehlab
    • 1
  • Keigo Watanabe
    • 2
  1. 1.Faculty of Electrical EngineeringK.N. Toosi UniversityTehranIran
  2. 2.Department of Mechanical EngineeringSaga UniversityJapan

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