Radial Basis Function Neural Network on Julia

2014
Andrejs Bondarenko, Arkādijs Borisovs

Rise of Julia as a technical computing language usually is regarded to its fluency, fun factor and mainly execution speed. Although at current stage Julia can not compete with R or Matlab in sense of amount of available libraries, new libraries are constantly added. Current abstract aims at presentation of Radial Basis Function Neural Network (RBFNN) Julia implementation and some execution tests which clearly show that Julia not optimized version outperforms Matlab version by sixty percent in terms of execution time.

Atslēgas vārdi
Julia, Radial Basis Function, neural network
Hipersaite
https://www.researchgate.net/publication/268746135_Radial_Basis_Function_Neural_Network_on_Julia_%28accepted_but_not_published%29

Bondarenko, A., Borisovs, A. Radial Basis Function Neural Network on Julia [tiešsaiste]. 2014. Pieejams: https://www.researchgate.net/publication/268746135_Radial_Basis_Function_Neural_Network_on_Julia_%28accepted_but_not_published%29.

Publikācijas valoda
English (en)