The UD RLS algorithm for training feedforward neural networks
International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 1, p. 115.
Voir la notice de l'article dans European Digital Mathematics Library
A new algorithm for training feedforward multilayer neural networks is proposed. It is based on recursive least squares procedures and U-D factorization, which is a well-known technique in filter theory. It will be shown that due to the U-D factorization method, our algorithm requires fewer computations than the classical RLS applied to feedforward multilayer neural network training.
Classification :
93E24, 68T05, 94A99
Mots-clés : learning algorithms, neural networks, UD factorization, recursive least squares method
Mots-clés : learning algorithms, neural networks, UD factorization, recursive least squares method
@article{IJAMCS_2005__15_1_207720, author = {Jaros{\l}aw Bilski}, title = {The {UD} {RLS} algorithm for training feedforward neural networks}, journal = {International Journal of Applied Mathematics and Computer Science}, pages = {115}, publisher = {mathdoc}, volume = {15}, number = {1}, year = {2005}, zbl = {1103.68673}, language = {en}, url = {https://geodesic-test.mathdoc.fr/item/IJAMCS_2005__15_1_207720/} }
TY - JOUR AU - Jarosław Bilski TI - The UD RLS algorithm for training feedforward neural networks JO - International Journal of Applied Mathematics and Computer Science PY - 2005 SP - 115 VL - 15 IS - 1 PB - mathdoc UR - https://geodesic-test.mathdoc.fr/item/IJAMCS_2005__15_1_207720/ LA - en ID - IJAMCS_2005__15_1_207720 ER -
%0 Journal Article %A Jarosław Bilski %T The UD RLS algorithm for training feedforward neural networks %J International Journal of Applied Mathematics and Computer Science %D 2005 %P 115 %V 15 %N 1 %I mathdoc %U https://geodesic-test.mathdoc.fr/item/IJAMCS_2005__15_1_207720/ %G en %F IJAMCS_2005__15_1_207720
Jarosław Bilski. The UD RLS algorithm for training feedforward neural networks. International Journal of Applied Mathematics and Computer Science, Tome 15 (2005) no. 1, p. 115. https://geodesic-test.mathdoc.fr/item/IJAMCS_2005__15_1_207720/