Neural Network Architectures and Learning Schemes

  • N. B. Karayiannis
  • A. N. Venetsanopoulos
Chapter
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 209)

Abstract

Existing neural network architectures can be divided into three basic categories: Feed forward, Feed-back, and Self-organizing neural networks. The most widely used neural architectures that can be classified into these three categories are shown in Figure 2.1. Although each of these categories is based on a different philosophy and obeys different principles, the characterization of a system by the general term “neural network” usually implies an ability to learn. Learning is the process by which a neural system acquires ability to carry out certain tasks by adjusting its internal parameters according to some learning scheme. Depending on the particular neural architecture considered, learning can be either supervised or unsupervised.

Keywords

Neural Network Artificial Neural Network Synaptic Weight Hide Unit Output Unit 
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 New York 1993

Authors and Affiliations

  • N. B. Karayiannis
    • 1
  • A. N. Venetsanopoulos
    • 2
  1. 1.University of HoustonUSA
  2. 2.University of TorontoCanada

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