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Feedback Neural Networks

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

Abstract

The artificial neural networks discussed in this chapter have different architecture from that of the feedforward neural networks introduced in the last chapter. That is, there are inherent feedback connections between the neurons of the networks. For the feedforward neural networks, such as the simple or multilayer perceptrons, the feedback-type interactions do occur during their learning, or training, stage. Information about the weight adjustment is fed back to the various layers from the output layer to reduce the overall output error with regard to the known input-output experience. When the training stage ends, the feedback interaction within the network no longer remains.

Keywords

Energy Function Feedforward Neural Network Hadamard Matrix Connection Matrix Attraction Basin 
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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