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J. Math.</a> <a href="/?q=in%3A520172" title="Articles in this Issue">21, No. 7, Paper No. 199, 24 p. (2024)</a>. </div> <div class="abstract">Summary: The main aim of the present paper is to provide a full asymptotic analysis of a family of neural network (NN) operators based on suitable density functions within the \(L^p\)-setting, and in the space of continuous functions. Two approaches are pursued: the first employs the celebrated Hardy-Littlewood (HL) maximal inequality, while the second adopts a constructive, fully moment-based, method. A crucial step in the proof of the previous results is provided by achieving asymptotic estimates for the NN operators in the cases of functions belonging to Sobolev spaces. By means of the previously mentioned first approach, we are able to establish sharp estimates that can not be applied with \(p=1\), since in that case, the HL maximal inequality fails. This justifies resorting to the second complementary approach, which is revealed to be very useful to cover the remaining case. The asymptotic analysis is finally completed by deducing the corresponding qualitative order of approximation for functions within suitable Lipschitz classes. At the end of the paper, several examples of density functions are also presented and discussed in relation to the previous results. Finally, we recall that NN operators based on the well-known ReLU or RePUs functions are also included in the present theory.</div> <div class="clear"></div> <br> <div class="citations"><div class="clear"><a href="/?q=rf%3A7954629">Cited in <strong>3</strong> Documents</a></div></div> <div class="classification"> <h3>MSC:</h3> <table><tr> <td> <a class="mono" href="/classification/?q=cc%3A47A58" title="MSC2020">47A58</a> </td> <td class="space"> Linear operator approximation theory </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A47A63" title="MSC2020">47A63</a> </td> <td class="space"> Linear operator inequalities </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A47A57" title="MSC2020">47A57</a> </td> <td class="space"> Linear operator methods in interpolation, moment and extension problems </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A41A25" title="MSC2020">41A25</a> </td> <td class="space"> Rate of convergence, degree of approximation </td> </tr><tr> <td> <a class="mono" href="/classification/?q=cc%3A41A05" title="MSC2020">41A05</a> </td> <td class="space"> Interpolation in approximation theory </td> </tr></table> </div><div class="keywords"> <h3>Keywords:</h3><a href="/?q=ut%3Aneural+network+operators">neural network operators</a>; <a href="/?q=ut%3Aasymptotic+analysis">asymptotic analysis</a>; <a href="/?q=ut%3AHardy-Littlewood+maximal+inequality">Hardy-Littlewood maximal inequality</a>; <a href="/?q=ut%3ASobolev+spaces">Sobolev spaces</a>; <a href="/?q=ut%3Asigmoidal+functions">sigmoidal functions</a>; <a href="/?q=ut%3AReLU">ReLU</a>; <a href="/?q=ut%3ARePUs">RePUs</a></div> <!-- Modal used to show zbmath metadata in different output formats--> <div class="modal fade" id="metadataModal" tabindex="-1" role="dialog" aria-labelledby="myModalLabel"> <div class="modal-dialog" role="document"> <div class="modal-content"> <div class="modal-header"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">&times;</span></button> <h4 class="modal-title" id="myModalLabel">Cite</h4> </div> <div class="modal-body"> <div class="form-group"> <label for="select-output" class="control-label">Format</label> <select id="select-output" class="form-control" aria-label="Select Metadata format"></select> </div> <div class="form-group"> <label for="metadataText" class="control-label">Result</label> <textarea class="form-control" id="metadataText" rows="10" style="min-width: 100%;max-width: 100%"></textarea> </div> <div id="metadata-alert" class="alert alert-danger" role="alert" style="display: none;"> <!-- alert for connection errors etc --> </div> </div> <div class="modal-footer"> <button type="button" class="btn btn-primary" onclick="copyMetadata()">Copy to clipboard</button> <button type="button" class="btn btn-default" data-dismiss="modal">Close</button> </div> </div> </div> </div> <div class="functions clearfix"> <div class="function"> <!-- Button trigger metadata modal --> <a type="button" class="btn btn-default btn-xs pdf" data-toggle="modal" data-target="#metadataModal" data-itemtype="Zbl" data-itemname="Zbl 07954629" data-ciurl="/ci/07954629" data-biburl="/bibtex/07954629.bib" data-amsurl="/amsrefs/07954629.bib" data-xmlurl="/xml/07954629.xml" > Cite </a> <a class="btn btn-default btn-xs pdf" data-container="body" type="button" href="/pdf/07954629.pdf" title="Zbl 07954629 as PDF">Review PDF</a> </div> <div class="fulltexts"> <span class="fulltext">Full Text:</span> <a class="btn btn-default btn-xs" type="button" href="https://doi.org/10.1007/s00009-024-02752-8" aria-label="DOI for “Asymptotic analysis of neural network operators employing the Hardy-Littlewood maximal inequality”" title="10.1007/s00009-024-02752-8">DOI</a> </div> <div class="sfx" style="float: right;"> </div> </div> <div class="references"> <h3>References:</h3> <table><tr> <td>[1]</td> <td class="space">Aldaz, JM; Colzani, L.; Pérez Lázaro, J., Optimal bounds on the modulus of continuity of the uncentered Hardy-Littlewood maximal function, J. 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