Voting procedures from the perspective of theory of neural networks
Ibragim Esenovich Suleimenov, Sergey Panchenko, Oleg Gabrielyan, Ivan Pak · Open Engineering · 2016
Abstract It is shown that voting procedure in any authority can be treated as Hopfield neural network analogue. It was revealed that weight coefficients of neural network which has discrete outputs −1 and 1 can be replaced by coefficients of a discrete set (−1, 0, 1). This gives us the opportunity to qualitatively analyze the voting procedure on the basis of limited data about mutual influence of members. It also proves that result of voting procedure is actually taken by network formed by voting members.