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Self-Organized Neural Networks

  • Xiang-Sun Zhang
Part of the Nonconvex Optimization and Its Applications book series (NOIA, volume 46)

Abstract

As introduced in previous chapters, adaptation or learning is the main feature of artificial neural networks. In Chapter 6 on feedforward network, the focus was on various algorithms for supervised learning in which learning process is carried out with training data which is consisting of specific inputs and the desired outputs or intermediate states of the network. The training data is given by the supervisor of the network or an external signal source. For feedback networks (non-adaptive nets) discussed in Chapter 7, the weights are predetermined by the network designer for solving a specific problem. In this chapter we briefly introduce the concept of self-organized learning or unsupervised learning.

Keywords

Output Layer Input Pattern Synaptic Weight Feedforward Network Neighborhood Function 
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 2000

Authors and Affiliations

  • Xiang-Sun Zhang
    • 1
  1. 1.Academy of Mathematics and Systems, Institute of Applied MathematicsChinese Academy of SciencesChina

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