Models of Self-correlation Type Complex-Valued Associative Memories and Their Dynamics

  • Yasuaki Kuroe
  • Yuriko Taniguchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)


Associative memories are one of the popular applications of neural networks and several studies on their extension to the complex domain have been done. One of the important factors to characterize behavior of a complex-valued neural network is its activation function which is a nonlinear complex function. We have already proposed a model of self-correlation type associative memories using complex-valued neural networks with one of the most commonly used activation function. In this paper, we propose two additional models using different nonlinear complex functions and investigated their behaviors as associative memories theoretically. Comparisons are also made among these three models in terms of dynamics and storage capabilities.


Equilibrium Point Activation Function Nonlinear Dynamical System Associative Memory Hadamard Matrice 
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-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Yasuaki Kuroe
    • 1
  • Yuriko Taniguchi
    • 2
  1. 1.Center for Information Science 
  2. 2.Department of Electronics and Information ScienceKyoto Institute of TechnologyKyotoJapan

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