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Reseach Article

Differential learning algorithm for Artificial Neural Networks

by Manjunath R, Shyam vasudev, Narendranath Udupa
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 18
Year of Publication: 2010
Authors: Manjunath R, Shyam vasudev, Narendranath Udupa
10.5120/381-571

Manjunath R, Shyam vasudev, Narendranath Udupa . Differential learning algorithm for Artificial Neural Networks. International Journal of Computer Applications. 1, 18 ( February 2010), 65-70. DOI=10.5120/381-571

@article{ 10.5120/381-571,
author = { Manjunath R, Shyam vasudev, Narendranath Udupa },
title = { Differential learning algorithm for Artificial Neural Networks },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 18 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 65-70 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number18/381-571/ },
doi = { 10.5120/381-571 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:46:43.601017+05:30
%A Manjunath R
%A Shyam vasudev
%A Narendranath Udupa
%T Differential learning algorithm for Artificial Neural Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 18
%P 65-70
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Artificial Neural Networks (ANN) are extremely useful to relate the nonlinearly depending outputs with the inputs. Various architectures are available for the ANNs to speedup the training period and reduce the square error. In this paper, new classes of neural networks with differential feedback are presented. The different orders of differential feed back form a manifold of hyperplanes. Interesting properties of this differentially fed ANN (DANN) are derived through these hyperplanes.

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Index Terms

Computer Science
Information Sciences

Keywords

Adaptive control Neural networks Differential feedback Multiresolution