Optimization of Neural Network Identification of a Non-Stationary Object Based On Spline Functions

Authors

  • Jumanov Isroil Ibragimovich Doctor of Technical Sciences, Professor, Department of Information Technologies, Samarkand State University, Samarkand, Uzbekistan
  • Djuraev Botir Abdusalyamovich Graduate student, Department of Information Technologies, Samarkand State University, Samarkand, Uzbekistan

Keywords:

identification, non-stationary object, spline function, neural network, optimization, recognition, forecasting

Abstract

A technique for smoothing a dynamic process based on basis-spline functions and calculating information recovery coefficients has been developed, which helps to optimize the training of a neural network data processing system by reducing the errors of the training subset. Methods and algorithms for modeling the processes of smoothing, processing, and restoring data of non-stationary processes based on cubic spline functions are studied.

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Published

2022-02-16

How to Cite

Ibragimovich, J. I. ., & Abdusalyamovich, D. B. . (2022). Optimization of Neural Network Identification of a Non-Stationary Object Based On Spline Functions. International Journal of Innovative Analyses and Emerging Technology, 2(2), 49–55. Retrieved from https://oajournals.net/index.php/ijiaet/article/view/1021

Issue

Vol. 2 No. 2 (2022): February

Section

Articles

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