Designing high-performance microstrip quad-band bandpass filters (for multi-service communication systems): a novel method based on artificial neural networks

Recently, high-performance multi-channel microstrip filters are widely demanded by modern multi-service communication systems. Designing these filters with both compact size and low loss is a challenge for the researchers. In this paper and for the first time, we have proposed a novel method based o...

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Published inNeural computing & applications Vol. 34; no. 10; pp. 7507 - 7521
Main Authors Rezaei, Abbas, Yahya, Salah I., Noori, Leila, Jamaluddin, Mohd Haizal
Format Journal Article
LanguageEnglish
Published London Springer London 01.05.2022
Springer Nature B.V
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Abstract Recently, high-performance multi-channel microstrip filters are widely demanded by modern multi-service communication systems. Designing these filters with both compact size and low loss is a challenge for the researchers. In this paper and for the first time, we have proposed a novel method based on artificial neural network to design and simulate multichannel microstrip bandpass filters. For this purpose, the frequency, physical dimensions, and substrate parameters, i.e., type and thickness, of the BPF are selected as the inputs and the S -parameters, i.e. S 11 and S 21, are selected as the outputs of the proposed model. Using an accurate multilayer perceptron neural network trained with back-propagation technique, a high-performance microstrip quad-band bandpass filter (QB-BPF) is designed which has a novel compact structure consisting of meandrous spirals, coupled lines, and patch feeds. The proposed method can be easily used for designing other microstrip devices such as filters, couplers, and diplexers. The designed filter occupies a very small area of 0.0012 λ g 2 , which is the smallest size in comparison with previously published works. It operates at 0.7, 2.2, 3.8, and 5.6 GHz for communication systems. The low insertion loss, high return losses, low group delay, and good frequency selectivity are obtained. To verify the design method and simulation results, the introduced filter is fabricated and measured. The results show an agreement between the simulation and measurement.
AbstractList Recently, high-performance multi-channel microstrip filters are widely demanded by modern multi-service communication systems. Designing these filters with both compact size and low loss is a challenge for the researchers. In this paper and for the first time, we have proposed a novel method based on artificial neural network to design and simulate multichannel microstrip bandpass filters. For this purpose, the frequency, physical dimensions, and substrate parameters, i.e., type and thickness, of the BPF are selected as the inputs and the S-parameters, i.e. S11 and S21, are selected as the outputs of the proposed model. Using an accurate multilayer perceptron neural network trained with back-propagation technique, a high-performance microstrip quad-band bandpass filter (QB-BPF) is designed which has a novel compact structure consisting of meandrous spirals, coupled lines, and patch feeds. The proposed method can be easily used for designing other microstrip devices such as filters, couplers, and diplexers. The designed filter occupies a very small area of 0.0012 λg2, which is the smallest size in comparison with previously published works. It operates at 0.7, 2.2, 3.8, and 5.6 GHz for communication systems. The low insertion loss, high return losses, low group delay, and good frequency selectivity are obtained. To verify the design method and simulation results, the introduced filter is fabricated and measured. The results show an agreement between the simulation and measurement.
Recently, high-performance multi-channel microstrip filters are widely demanded by modern multi-service communication systems. Designing these filters with both compact size and low loss is a challenge for the researchers. In this paper and for the first time, we have proposed a novel method based on artificial neural network to design and simulate multichannel microstrip bandpass filters. For this purpose, the frequency, physical dimensions, and substrate parameters, i.e., type and thickness, of the BPF are selected as the inputs and the S -parameters, i.e. S 11 and S 21, are selected as the outputs of the proposed model. Using an accurate multilayer perceptron neural network trained with back-propagation technique, a high-performance microstrip quad-band bandpass filter (QB-BPF) is designed which has a novel compact structure consisting of meandrous spirals, coupled lines, and patch feeds. The proposed method can be easily used for designing other microstrip devices such as filters, couplers, and diplexers. The designed filter occupies a very small area of 0.0012 λ g 2 , which is the smallest size in comparison with previously published works. It operates at 0.7, 2.2, 3.8, and 5.6 GHz for communication systems. The low insertion loss, high return losses, low group delay, and good frequency selectivity are obtained. To verify the design method and simulation results, the introduced filter is fabricated and measured. The results show an agreement between the simulation and measurement.
Author Rezaei, Abbas
Yahya, Salah I.
Noori, Leila
Jamaluddin, Mohd Haizal
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CitedBy_id crossref_primary_10_1016_j_aeue_2024_155228
crossref_primary_10_14500_aro_11620
crossref_primary_10_3390_s23073775
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Keywords Multilayer perceptron
Artificial neural network
Microstrip
Quad-band bandpass filter
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Snippet Recently, high-performance multi-channel microstrip filters are widely demanded by modern multi-service communication systems. Designing these filters with...
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springer
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StartPage 7507
SubjectTerms Artificial Intelligence
Artificial neural networks
Back propagation networks
Bandpass filters
Communication
Communications systems
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Design
Diplexers
Electronic filters
Group delay
Image Processing and Computer Vision
Insertion loss
Microstrip devices
Multilayer perceptrons
Neural networks
Original Article
Parameters
Probability and Statistics in Computer Science
Selectivity
Simulation
Spirals
Substrates
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Title Designing high-performance microstrip quad-band bandpass filters (for multi-service communication systems): a novel method based on artificial neural networks
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