Algebraic properties of recurrent neural networks of discrete time
Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 5 (2005) no. 1-2, pp. 116-128
Cet article a éte moissonné depuis la source Math-Net.Ru
Artificial neural networks сan be used effectively for а quite general class of problems. Still there exists nо formal foundation of some important constructions used in the theory. ln this paper an attempt is undertaken to formalize some concepts of neuroinformatics and consider their properties from the point of view of applied universal algebra. lt is proposed to treat neural networks as heterogeneous algebras which has made it possible to prove for them basic results similar to algebraic theorems оn homomorphisms and congruences.
@article{ISU_2005_5_1-2_a11,
author = {I. I. Slepovichev},
title = {Algebraic properties of recurrent neural networks of discrete time},
journal = {Izvestiya of Saratov University. Mathematics. Mechanics. Informatics},
pages = {116--128},
year = {2005},
volume = {5},
number = {1-2},
language = {ru},
url = {http://geodesic.mathdoc.fr/item/ISU_2005_5_1-2_a11/}
}
TY - JOUR AU - I. I. Slepovichev TI - Algebraic properties of recurrent neural networks of discrete time JO - Izvestiya of Saratov University. Mathematics. Mechanics. Informatics PY - 2005 SP - 116 EP - 128 VL - 5 IS - 1-2 UR - http://geodesic.mathdoc.fr/item/ISU_2005_5_1-2_a11/ LA - ru ID - ISU_2005_5_1-2_a11 ER -
I. I. Slepovichev. Algebraic properties of recurrent neural networks of discrete time. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 5 (2005) no. 1-2, pp. 116-128. http://geodesic.mathdoc.fr/item/ISU_2005_5_1-2_a11/
[1] McCulloc W. S., Pitts W. N., “A logical calculus of the ideas immanent in nervous activity”, Bull. of Math. Biophysics, 5 (1943), 115–133 | DOI | MR | Zbl
[2] Psiola V. V., “Obzor osnovnykh neirosetevykh modelei”, Intellektualnye sistemy, 4:3–4 (1999), 139–172
[3] Bogomolov A. M., Salii V. N., Algebraicheskie osnovy teorii diskretnykh sistem, M., 1997, 11
[4] Carrasco R. C., Mikel J. O., Forcada L., Efficient Encodings of finite automata in discrete-time recurrent neural networks, http://www.dlsi.ua.es/m̃lf/docum/carrasco99p.pdf