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Model and Principles for the Implementation of Neural-Like Structures Based on Geometric Data Transformations

  • Roman Tkachenko
  • Ivan Izonin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 754)

Abstract

In this paper, the concept of information modeling based on a new model of geometric transformations is considered. This concept ensures the solutions of the following tasks like pattern recognition, predicting, classification, the principal independent components selection, optimization, recovering of lost data or their consolidation and implementing the information protection and privacy methods. Neural-like structures based on the Geometric Transformations Model as universal approximator implement principles of training and self-training and base on an algorithmic or hardware performing variants using the space and time parallelization principles. Geometric Transformations Model uses a single methodological framework for various tasks and fast non-iterative study with pre-defined number of computation steps, provides repeatability of the training outcomes and the possibility to obtain satisfactory solutions for large and small training samples.

Keywords

Geometric Transformations Model Neural-like network Training and self-training algorithms Basic properties of the geometric transformations model 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Lviv Polytechnic National UniversityLvivUkraine

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