ANN Model for Designing Stub Microstrip LowPass Filters

Using neural network technology, the design technology of the stubbed microstrip low-pass filter is given. The required length of the microstrip filter is used to obtain the correlation between inputs in the neural network. This article presents the design and analysis of stub microstrip low-pass fi...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Proceedings (International Conference on Communication Systems and Network Technologies Online) S. 22 - 28
Hauptverfasser: Kushwah, Vivek Singh, Charyulu, M.L.N., Tomar, Geetam Singh
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 06.04.2024
Schlagworte:
Artificial neural networks
Computational modeling
CPW
Cutoff frequency
Epochs
Feed-forward Backpropagation Algorithm
Insertion-loss
Low-pass filters
Mathematical models
Microstrip filters
MSE
Software algorithms
ISSN:2473-5655
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Using neural network technology, the design technology of the stubbed microstrip low-pass filter is given. The required length of the microstrip filter is used to obtain the correlation between inputs in the neural network. This article presents the design and analysis of stub microstrip low-pass filter with cut-off frequency of 1.48\text{GHz} with insertion loss (S21) of -3{\text{dB}} , and a return loss of -2.59{\text{dB}} . Then, a neural network model was designed to find the difference in the irregularity (S-parameters) of the microstrip low-pass filter with a 1.48\text{GHz} range for \mathrm{L} -band applications. After the neural model of the microstrip low-pass filter was developed, its design was shown to be more feasible and precise as compared to simulation software. MATLAB programming language and industrial software, IE3D 14.1, are utilized to train the neural network for obtaining precise dimensions after performing simulation.
ISSN:2473-5655
DOI:10.1109/CSNT60213.2024.10545917