This article examines neural network-based approximations for the superhedging price process of a contingent claim in discrete time market model. First we prove that α-quantile hedging converges to at 0 α tending 1, and show can be approximated by price. provides approximation also strategy up maturity. To obtain t > $t>0$ , using Doob decomposition, it is sufficient determine consumption. We e...

this paper is concerned with the consensus tracking problem of high order mimo nonlinear multi-agent systems. the agents must follow a leader node in presence of unknown dynamics and uncertain external disturbances. the communication network topology of agents is assumed to be a fixed undirected graph. a distributed adaptive control method is proposed to solve the consensus problem utilizing re...

In this paper, modular neural network (MNN) inversion has been applied for the parameters approximation of the gravity anomaly causative target. The trained neural network is used for estimating the amplitude coefficient and depths to the top and bottom of a finite vertical cylinder source. The results of the applied neural network method are compared with the results of the least-squares stand...