Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Data Sciences
Akanova, Akerke
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Computer Simulation of Control of High-Order Nonlinear Systems using Feedback Bakhadirova, Gulnaz; Tasbolatuly, Nurbolat; Tanirbergenova, Alua; Dautova, Aigul; Akanova, Akerke; Ulikhina, Yuliya
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.275

Abstract

The relevance of the topic stated in this research is the need to develop and implement methods of computer modelling of these control systems. The purpose of this research is the search for opportunities for controlling nonlinear systems and the creation of a computer model for controlling nonlinear systems. The basis of the methodological approach in this research work is a combination of methods of theoretical and applied research of general principles of construction of computer models of control of high order nonlinear systems by means of feedback. In the course of the research work, the results were obtained, indicating the effectiveness of the development of an algorithm for finding the control of tracking a given reference signal of nonlinear systems and nonlinear systems with time delay. An algorithm has been developed to find a control that can effectively track the output signal of a nonlinear system behind a given reference signal. In addition, a scientific analysis of the tracking and stabilization errors of nonlinear systems and time-delayed nonlinear systems has been carried out depending on the control parameters, and graphical representations of a computer model of numerical experiments performed according to the control algorithms have been presented. It is established that the output control problem for a nonlinear system is to obtain a feedback control that forces the controlled output signal of the nonlinear system to asymptotically track the reference signal. The practical significance of the obtained results lies in the possibility of their use in the creation of computer models of process control with feedback.
Modelling a neural network for analysing the results of segmentation of satellite images Kaldarova, Mira; Akanova, Akerke; Naizagarayeva, Akgul; Kazanbayeva, Albina; Ospanova, Nazira
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp614-621

Abstract

The study's relevance lies in addressing inaccuracies within satellite image segmentation, necessitating the development and implementation of neural network models for automated segmentation. The purpose of study is to develop a model of a neural network for training with data obtained from the segmentation of satellite images. The basis of the methodological approach in study is a combination of methods of system analysis of neural networks, which have had a substantial impact on the development of the computer vision industry, with an empirical study of the general principles of neural network modelling for the training on satellite images segmentation. In this study, the results were obtained, indicating that there is a fundamental possibility of developing and practical implementation of a neural network model to determine the quality of the obtained segmentation of images of agricultural fields. Satellite images of agricultural fields of the Republic of Kazakhstan are obtained, and segmentation of field images is performed using the developed neural network model for learning segmentation results. The practical importance of the results obtained in study lies in the possibility of their use in the development of functional models of neural networks for training the results of the segmentation of satellite images.
Modelling and controlling outputs of nonlinear systems using feedback Bahadirova, Gulnaz; Tasbolatuly, Nurbolat; Akanova, Akerke; Muratova, Gulzhan; Sadykova, Anar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp540-549

Abstract

This study aimed to analyze methods for modeling and controlling the output of nonlinear systems using feedback, analytical methods, mathematical modeling, and differential equation theory. Key findings include the mathematical characterization of equations and the analysis of system stability and asymptotic behavior. The study explored various methods for addressing problems in nonlinear systems, emphasizing the importance of identifying effective solutions. The research highlights the significance of developing effective approaches to solving complex problems involving nonlinear systems. Feedback is essential for controlling and correcting dynamic processes in systems with nonlinearities. The study’s key finding is the mathematical characterization of equations describing nonlinear systems, providing insight into system structure and behavior under different parameters. Analyzing stability and asymptotic behavior allows for assessing system reliability and predicting long-term stability. This study contributes to the scientific understanding and development of methods for modeling and controlling nonlinear systems using feedback.
Co-Authors Bahadirova, Gulnaz Bakhadirova, Gulnaz Dautova, Aigul Kaldarova, Mira Kazanbayeva, Albina Muratova, Gulzhan Naizagarayeva, Akgul Ospanova, Nazira Sadykova, Anar Tanirbergenova, Alua Tasbolatuly, Nurbolat Ulikhina, Yuliya