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All Journal International Journal of Electrical and Computer Engineering Bulletin of Electrical Engineering and Informatics
Kaliyev, Zhanybek
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Computer Science & IT Electrical & Electronics Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

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Analytical broadband impedance matching using modified approximating functions with embedded transmission zeros Yerzhan, Assel; Manbetova, Zhanat; Mussapirova, Gulzada; Karnakova, Gayni; Mukhamejanova, Almira; Imankul, Manat; Kaliyev, Zhanybek; Bakirova, Nagima
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11220

Abstract

This paper proposes a modified approximating function (MAF)-based analytical method for broadband impedance matching in radio-electronic systems. Unlike traditional Chebyshev and Butterworth approaches, which rely on fixed pole distributions and predefined amplitude responses, the proposed method analytically embeds load-specific transmission zeros directly into the approximation function. This modification enables more accurate reconstruction of frequency-dependent impedance behavior without increasing the network order or circuit complexity. The method establishes a unified analytical synthesis framework linking impedance modeling, ladder-network realization, and constrained optimization. Validation was performed over the 1–10 GHz band using numerical simulations, Monte Carlo tolerance analysis, and prototype measurements. Compared with classical Chebyshev and Butterworth designs, the MAF-based approach achieves a 15–25% reduction in maximum reflection coefficient, a 30–40% decrease in optimization iterations, and improved robustness, with reflection variations remaining within 2% under ±10% parameter deviations. The results confirm that the proposed method provides superior analytical flexibility, improved matching accuracy, and reduced computational effort, making it suitable for automated broadband radio frequency (RF) design applications.
Thermal mode modeling using neural network technologies and the finite element method Mussabekov, Nazarbek; Utepbergenov, Irbulat; Kaliyev, Zhanybek; Issayeva, Zhazira; Ybytayeva, Galiya; Ansabekova, Gulbakyt; Karnakova, Gayni; Butabaeva, Karlygash
Bulletin of Electrical Engineering and Informatics Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v15i2.11268

Abstract

This study presents the analysis and modeling of the thermal regime of a furnace lining at an industrial copper smelting facility using a combined approach based on neural network (NN) technologies and the finite element method (FEM). Experimental temperature data were collected from a laboratory setup equipped with three thermocouples (TP-2488/1 and TCRosemount 0065), with a sampling frequency of 1 Hz over a total duration of 5 hours, resulting in 18,000 measurement points. The measurement uncertainty of the thermocouples did not exceed ±1.5 °C. These data were used both for model development and for validating the numerical FEM simulations. A feedforward neural network was trained using 70% of the dataset, while 15% and 15% were used for validation and testing, respectively. The prediction error of the neural network remained within 3% with a 95% confidence interval of [2.6%, 3.4%]. The results show that the proposed hybrid approach improves temperature prediction accuracy and reduces static control error by 15% when combined with a proportional-integral controller. The methodology demonstrates significant potential for improving thermal process stability and reducing energy consumption in high-temperature metallurgical systems.
Co-Authors Abdimuratov, Zhubanyshbai Ansabekova, Gulbakyt Appakov, Nurbol Bakirova, Nagima Butabaeva, Karlygash Imankul, Manat Issayeva, Zhazira Karnakova, Gayni Manbetova, Zhanat Mukhamejanova, Almira Mussabekov, Nazarbek Mussapirova, Gulzada Sarbassova, Nurbanu Sarsikeyev, Yermek Umurzakova, Anara Utepbergenov, Irbulat Ybytayeva, Galiya Yerzhan, Assel Zhumatova, Assel