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Discrete Recurrent Neural Networks

  • Zhang Yi
  • K. K. Tan
Part of the Network Theory and Applications book series (NETA, volume 13)

Abstract

Generally, a discrete time RNN can be described by the difference system model
$$ x(k + 1) = F(x(k)) $$
(8.1)
where x ∈ R n , F : R n → R n is a mapping which can be bounded or unbounded. By suitably selecting the mapping F in (8.1), various classes of discrete RNNs can be obtained.

Keywords

Equilibrium Point Convergence Analysis Recurrent Neural Network Cellular Neural Network Complete Convergence 
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 2004

Authors and Affiliations

  • Zhang Yi
    • 1
  • K. K. Tan
    • 2
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduPeople’s Republic of China
  2. 2.Department of Electrical and Computer EngineeringThe National University of SingaporeSingapore

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