Forecast method based on the time-delay mean field boltzmann machine

Abstract

The problem of insufficient forecast efficiency for supply chain management is solved. A neural network forecast model based on the Time-Delay Mean Field Boltzmann Machine with time delays in the visible layer has been created. In the process of adjusting the structure of the developed model, the length of the hidden layer was determined, and the calculation of the model parameters was carried out on the basis of the parallel computing platform CUDA. Improving forecast accuracy and speed of calculations makes it possible to improve the quality of the forecast, resulting in increased supply flexibility and reduced logistics costs. A software toolkit based on the Matlab package has been developed, which makes it possible to implement the proposed method. The developed software tools are used to solve the problem of supply chains forecasting