Function approximation-fast-convergence neural approach based on spectral analysis. Abstract: We propose a constructive approach to building single-hidden ...
A spectrum-based learning procedure is introduced that minimizes the difference between the spectrum of the training data and the Spectrum of the network's ...
title = {Function approximation-fast-convergence neural approach based on spectral analysis}, journal = {IEEE Transactions on Neural Networks}, volume = {10} ...
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What is function approximation in a neural network?
Sep 27, 2023 · Function approximation-fast-convergence neural approach based on spectral analysis. ... Neural spectral composition for function approximation.
Jun 9, 2022 · Abstract:Value approximation using deep neural networks is at the heart of off-policy deep reinforcement learning, and is often the primary ...
An innovative neural-based approach for function approximation is proposed by means of the spectral analysis of the function y(x) to be approximated.
Jan 8, 2022 · So this is probably a basic question. If the main premise of neural networks is that they are global function approximators, what advantage ...
In this paper, we proposed an innovative neural based approach for the function approximation problem. Its design is based on the spectral analysis of the ...
Oct 1, 2023 · First, we present quantitative insights into the tradeoff between the number of neurons and the amount of spectral geometric information a ...
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Function approximation-fast-convergence neural approach based on spectral analysis ... Neural Networks for Function Approximation · Author Picture Andrea ...