Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Kryzhanovsky, B. V."'
Autor:
Kryzhanovsky, B. V., Litinskii, L. B.
We examine connection matrices of Ising systems with long-rang interaction on d-dimensional hypercube lattices of linear dimensions L. We express the eigenvectors of these matrices as the Kronecker products of the eigenvectors for the one-dimensional
Externí odkaz:
http://arxiv.org/abs/2008.04227
Autor:
Kryzhanovsky, B. V., Litinskii, L. B.
For a 1D Ising model, we obtained an exact expression for the spectral density in an n-vicinity of the ground state and explained why our n-vicinity method with the Gaussian approximation of the spectral density did not applicable in this case. We al
Externí odkaz:
http://arxiv.org/abs/1811.08703
Numerical methods are used to examine the thermodynamic characteristics of the two-dimensional Ising model as a function of the number of spins N. Onsager's solution is generalized to a finite-size lattice, and experimentally validated analytical exp
Externí odkaz:
http://arxiv.org/abs/1706.02541
Autor:
Kryzhanovsky, B. V.
The relationship between the spectral density and free energy of a spin system is considered. The analytical expressions allowing for the calculation of the spectral density for solvable models are determined. A linear Ising model is taken for testin
Externí odkaz:
http://arxiv.org/abs/1704.01351
Autor:
Egorov, V. I., Kryzhanovsky, B. V.
Publikováno v:
Optical Memory & Neural Networks; Sep2024, Vol. 33 Issue 3, p302-307, 6p
Autor:
Kryzhanovsky, B. V.
Publikováno v:
Optical Memory & Neural Networks; Sep2024, Vol. 33 Issue 3, p259-263, 5p
In this paper we develop a formalism allowing us to describe operating of a network based on the parametrical four-wave mixing process that is well-known in nonlinear optics. The recognition power of a network using parametric neurons operating with
Externí odkaz:
http://arxiv.org/abs/1208.1774
Significant changes of the relative permittivity of a silver film have been detected using the surface plasmon resonance (SPR) method when a constant electric field is applied to a MDM (metal-dielectric-metal) nanostructure. The structure looks like
Externí odkaz:
http://arxiv.org/abs/1204.6400
An effective neural network algorithm of the perceptron type is proposed. The algorithm allows us to identify strongly distorted input vector reliably. It is shown that its reliability and processing speed are orders of magnitude higher than that of
Externí odkaz:
http://arxiv.org/abs/cs/0412110
We consider two models of Hopfield-like associative memory with $q$-valued neurons: Potts-glass neural network (PGNN) and parametrical neural network (PNN). In these models neurons can be in more than two different states. The models have the record
Externí odkaz:
http://arxiv.org/abs/cond-mat/0412680