Soft Computing

, Volume 17, Issue 2, pp 265–273

Hebbian and error-correction learning for complex-valued neurons


DOI: 10.1007/s00500-012-0891-8

Cite this article as:
Aizenberg, I. Soft Comput (2013) 17: 265. doi:10.1007/s00500-012-0891-8


In this paper, we observe some important aspects of Hebbian and error-correction learning rules for complex-valued neurons. These learning rules, which were previously considered for the multi-valued neuron (MVN) whose inputs and output are located on the unit circle, are generalized for a complex-valued neuron whose inputs and output are arbitrary complex numbers. The Hebbian learning rule is also considered for the MVN with a periodic activation function. It is experimentally shown that Hebbian weights, even if they still cannot implement an input/output mapping to be learned, are better starting weights for the error-correction learning, which converges faster starting from the Hebbian weights rather than from the random ones.


Complex-valued neural networks Derivative-free learning Multi-valued neuron Hebbian learning Error-correction learning XOR problem 

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Texas A&M University-Texarkana7101 University AveTexarkanaUSA

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