USING ARTIFICIAL NEURAL NETWORKS WITH RADIAL BASIS FUNCTIONS FOR SOLVING PREDICTION
Abstract
The features of the application of artificial neural networks with radial basis functions for the task of forecasting. We describe the prediction process. A comparison of prediction using neural networks with radial basis functions, which are trained by one-step and multi-step learning algorithm.
Downloads
How to Cite
Issue
Section
License
The authors who publish in this collection agree with the following terms:
• The authors reserve the right to authorship of their work and give the magazine the right to first publish this work under the terms of license CC BY-NC-ND 4.0 (with the Designation of Authorship - Non-Commercial - Without Derivatives 4.0 International), which allows others to freely distribute the published work with a mandatory reference to the authors of the original work and the first publication of the work in this magazine.
• Authors have the right to make independent extra-exclusive work agreements in the form in which they were published by this magazine (for example, posting work in an electronic repository of an institution or publishing as part of a monograph), provided that the link to the first publication of the work in this journal is maintained. .
• Journal policy allows and encourages the publication of manuscripts on the Internet (for example, in institutions' repositories or on personal websites), both before the publication of this manuscript and during its editorial work, as it contributes to the emergence of productive scientific discussion and positively affects the efficiency and dynamics of the citation of the published work (see The Effect of Open Access).
