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Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence

Saeed Roshani, Hossein Heshmati, Sobhan Roshani*

Corresponding Author:

Sobhan Roshani

Affiliation(s):

Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah, Iran

*Corresponding Author: Sobhan Roshani, Email: [email protected]

Abstract:

In this paper, a lowpass – bandpass dual band microwave filter is designed by using deep learning and artificial intelligence. The designed filter has compact size and desirable pass bands. In the proposed filter, the resonators with Z-shaped and T-shaped lines are used to design the low pass channel, while coupling lines, stepped impedance resonators and open ended stubs are utilized to design the bandpass channel. Artificial neural network (ANN) and deep learning (DL) technique has been utilized to extract the proposed filter transfer function, so the values of the transmission zeros can be located in the desired frequency. This technique can also be used for the other electrical devices. The lowpass channel cut off frequency is 1 GHz, with better than 0.2 dB insertion loss. Also, the bandpass channel main frequency is designed at 2.4 GHz with 0.5 dB insertion loss in the passband.

Keywords:

Artificial neural network (ANN), deep learning (DL), dual band, lowpass – bandpass, microwave filter

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Cite This Paper:

Saeed Roshani, Hossein Heshmati, Sobhan Roshani (2021). Design of a Microwave Lowpass – Bandpass Filter using Deep Learning and Artificial Intelligence. Journal of the Institute of Electronics and Computer, 3, 1-16. https://doi.org/10.33969/JIEC.2021.31001.

References:

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