doi: 10.7392/openaccess.45011866
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.
Citation: Fialkiewicz, 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: [email protected]
Mobile with no support for PDF files? Switch to horizontal mode or download this paper. Other option: cloud download.
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).
Research registered in the DOI resolution system as: 10.7392/openaccess.45011866.
This domain has expired and been acquired by SerpNames.com. We have restored an archived version of this website using materials from Archive.org to preserve its historical and SEO value.
This is not the active website of the former owner or organization. We are not affiliated with or endorsed by any prior operators or related entities that may still be active elsewhere.
All content displayed here was publicly available prior to expiration and is presented under fair use for informational and archival purposes only.
Our intention is solely to maintain the domain’s historical context and not to harm or misrepresent any business, organization, or individual. No logins, payments, or personal data are collected or processed on this page.
If you are the previous owner or wish to request content removal, please contact team@serpnames.com.