Using the Single-Term Haar Wavelet to Solve the State Variable Models of nth-Order Circuits

This study is dedicated to investigating the utilization of the single-term Haar wavelet approach for solving a state variable model of nth-order circuits. We provide the matrix calculations in a simplified manner to facilitate comprehension. Furthermore, a numerical mathematical illustration is presented, demonstrating the application of this technique to solving two first-order differential equations with both constant and variable coefficients. The study’s findings unequivocally demonstrate that the suggested approach surpasses previous methods, providing greater levels of accuracy and precision. Moreover, the single-term Haar wavelet approach is exceptionally user-friendly and efficient, necessitating only a minimal amount of processing resources.

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Publication Details

Journal
Romanian Journal of Physics
Published
2024-09-15
DOI
https://doi.org/10.59277/romjphys.2024.69.108
Primary Topic
Model Reduction and Neural Networks
Type
article
Field-Weighted Citation Impact
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article

Using the Single-Term Haar Wavelet to Solve the State Variable Models of nth-Order Circuits

Nouri Kazem, MOMENI ABDOLREZA, TORKZADEH LEILA
Romanian Journal of Physics
Model Reduction and Neural Networks
article

Using the Single-Term Haar Wavelet to Solve the State Variable Models of nth-Order Circuits

Nouri Kazem, MOMENI ABDOLREZA, TORKZADEH LEILA
article en

Abstract

This study is dedicated to investigating the utilization of the single-term Haar wavelet approach for solving a state variable model of nth-order circuits. We provide the matrix calculations in a simplified manner to facilitate comprehension. Furthermore, a numerical mathematical illustration is presented, demonstrating the application of this technique to solving two first-order differential equations with both constant and variable coefficients. The study’s findings unequivocally demonstrate that the suggested approach surpasses previous methods, providing greater levels of accuracy and precision. Moreover, the single-term Haar wavelet approach is exceptionally user-friendly and efficient, necessitating only a minimal amount of processing resources.

Romanian Journal of PhysicsVol. 69(7-8)
Openalex Percentile: Top 82%
Model Reduction and Neural Networks
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