June 14, 2024Open Access

An elementary proof of a universal approximation theorem

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Abstract

In this short note, we give an elementary proof of a universal approximation theorem for neural networks with three hidden layers and increasing, continuous, bounded activation function. The result is weaker than the best known results, but the proof is elementary in the sense that no machinery beyond undergraduate analysis is used.

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Chris Monico (Fri,) studied this question.

synapsesocial.com/papers/68e64d66b6db6435875ddb7c https://doi.org/https://doi.org/10.48550/arxiv.2406.10002

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