NDLI: Hebbian and error-correction learning for complex-valued neurons

Content Provider	Springer Nature Link
Author	Aizenberg, Igor
Copyright Year	2012
Abstract	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.
Starting Page	265
Ending Page	273
Page Count	9
File Format	PDF
ISSN	14327643
Journal	Soft Computing
Volume Number	17
Issue Number	2
e-ISSN	14337479
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-07-14
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Complex-valued neural networks Derivative-free learning Multi-valued neuron Hebbian learning Error-correction learning XOR problem Computational Intelligence Artificial Intelligence (incl. Robotics) Mathematical Logic and Foundations Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Software Geometry and Topology

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Hebbian and error-correction learning for complex-valued neurons