We construct and discuss a functional equation with contraction property. The solutions are real univariate polynomials. The series solving the natural fixed point iterations have immediate interpretation in terms of Neural Networks with recursive properties and controlled accuracy.
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Bruno Després 1; Matthieu Ancellin 2
@article{CRMATH_2020__358_9-10_1059_0, author = {Bruno Despr\'es and Matthieu Ancellin}, title = {A functional equation with polynomial solutions and application to {Neural} {Networks}}, journal = {Comptes Rendus. Math\'ematique}, pages = {1059--1072}, publisher = {Acad\'emie des sciences, Paris}, volume = {358}, number = {9-10}, year = {2020}, doi = {10.5802/crmath.124}, language = {en}, }
TY - JOUR AU - Bruno Després AU - Matthieu Ancellin TI - A functional equation with polynomial solutions and application to Neural Networks JO - Comptes Rendus. Mathématique PY - 2020 SP - 1059 EP - 1072 VL - 358 IS - 9-10 PB - Académie des sciences, Paris DO - 10.5802/crmath.124 LA - en ID - CRMATH_2020__358_9-10_1059_0 ER -
%0 Journal Article %A Bruno Després %A Matthieu Ancellin %T A functional equation with polynomial solutions and application to Neural Networks %J Comptes Rendus. Mathématique %D 2020 %P 1059-1072 %V 358 %N 9-10 %I Académie des sciences, Paris %R 10.5802/crmath.124 %G en %F CRMATH_2020__358_9-10_1059_0
Bruno Després; Matthieu Ancellin. A functional equation with polynomial solutions and application to Neural Networks. Comptes Rendus. Mathématique, Volume 358 (2020) no. 9-10, pp. 1059-1072. doi : 10.5802/crmath.124. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.5802/crmath.124/
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