Complex-Valued Neurons with Phase-Dependent Activation Functions View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010

AUTHORS

Igor Aizenberg

ABSTRACT

In this paper, we observe two artificial neurons with complex-valued weights. There are a multi-valued neuron and a universal binary neuron. Both neurons have activation functions depending on the argument (phase) of the weighted sum. A multi-valued neuron may learn multiple-valued threshold functions. A universal binary neuron may learn arbitrary (not only linearly-separable) Boolean functions. It is shown that a multi-valued neuron with a periodic activation function may learn non-threshold functions by their projection to the space corresponding to the larger valued logic. A feedforward neural network with multi-valued neurons and its learning are also considered. More... »

PAGES

3-10

References to SciGraph publications

Book

TITLE

Artifical Intelligence and Soft Computing

ISBN

978-3-642-13231-5
978-3-642-13232-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-13232-2_1

DOI

http://dx.doi.org/10.1007/978-3-642-13232-2_1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029830487


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