Open Science Repository Computer and Information Sciences

doi: 10.7392/openaccess.45011866


Hypercube Neuron


Wojciech Fialkiewicz


Abstract

Classic perceptron is limited to classifying only data which is linearly separable. In this work it is proposed neuron that does not have that limitation. Using hypercube architecture it is possible to describe any logic function. Neuron using that kind of architecture can discover any logic function from train data. It is also possible to write into that kind of neuron any logic rule.

Keywords: Neuron, Hypercube, Multidimensional Activation Function, Neural Network, Hypercube Neuron.


CitationFialkiewicz, W. (2017). Hypercube Neuron. Open Science Repository Computer and Information Sciences, Online(open-access), 45011866e. http://doi.org/10.7392/OPENACCESS.45011866

Received: October 22, 2017

Published: October 26, 2017

Copyright: © 2015 Fialkiewicz, W. Creative Commons Attribution 3.0 Unported License.

Contact: research@open-science-repository.com


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Cite this paper

APA

Fialkiewicz, W. (2017). Hypercube Neuron. Open Science Repository Computer and Information Sciences, Online(open-access), 45011866e. http://doi.org/10.7392/OPENACCESS.45011866

MLA

Fialkiewicz, Wojciech. “Hypercube Neuron.” Open Science Repository Computer and Information Sciences Online.open-access (2017): 45011866e. Web. 26 Oct. 2017.

Chicago

Fialkiewicz, Wojciech. “Hypercube Neuron.” Open Science Repository Computer and Information Sciences Online, no. open-access (October 26, 2017): 45011866e. doi:10.7392/OPENACCESS.45011866.

Harvard

Fialkiewicz, W. (2017) ‘Hypercube Neuron’, Open Science Repository Computer and Information Sciences. Open Science Repository, Online(open-access), p. 45011866e. doi: 10.7392/OPENACCESS.45011866.

Science

1. W. Fialkiewicz, Hypercube Neuron, Open Sci. Repos. Comput. Inf. Sci. Online, 45011866e (2017).

Nature

1. Fialkiewicz, W. Hypercube Neuron. Open Sci. Repos. Comput. Inf. Sci. Online, 45011866e (2017).

doi

Research registered in the DOI resolution system as: 10.7392/openaccess.45011866.


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This work is licensed under a Creative Commons Attribution 3.0 Unported License.