Learning Capability of a Simple Neural Network
Abstract
We demonstrate that a neural network composed of only three nodes and three connections arranged in a 2-inputs, 1-middle, 1-output architecture is able to perform differentiation of univariate functions (Đ). Using a proposed empirical technique, we assess the network’s generalization capability by approximating a functional form for the growth function ΔF(N). We calculated the probability of error to be ≈10^-7 allowing us to justify the effectiveness of the simplistic approach in modeling a non-trivial task such as Đ.
Published
2012-07-03
How to Cite
JUANICO, Dranreb Earl; MONTEROLA, Christopher; SALOMA, Caesar.
Learning Capability of a Simple Neural Network.
Science Diliman: A Journal of Pure and Applied Sciences, [S.l.], v. 13, n. 2, july 2012.
ISSN 2012-0818.
Available at: <https://journals.upd.edu.ph/index.php/sciencediliman/article/view/186>. Date accessed: 04 aug. 2025.
Issue
Section
Articles
Keywords
architecture; differentiation; generalization; growth function
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