Abstract
The effects of adaptive genetic algorithms (AGAs) and defected ground structures (DGSs) on performance optimization of tapered microstrip filter are investigated. The proposed structure achieves an ultra wide stopband with high attenuation within a small surface area, as well as 45% smaller size, in comparison with conventional filters. The parameters of the filter are optimized using in-home AGA code. In the proposed AGA algorithm, the crossover and mutation probabilities are adaptively changed according to the value of individual fitness. Then by utilizing the proposed DGS, a compact S-band lowpass filter with ultra-wide spurious free window is obtained. The proposed filter achieves an insertion loss of 0.8 dB from DC up to 4 GHz and 21 dB rejection in the stopband from 4.3 up to 60 GHz. The fabricated and measured results exhibit good agreement with the simulated results. They demonstrate that combining AGA and DGS yields best possible response for this group of filters.
Highlights
High-performance microwave filters with minimized size and weight are playing an important role for design and fabrication of high-efficiency miniaturized microwave systems
We have proposed a high-performance and compact lowpass filter structure with ultra-wide stopband using adaptive genetic algorithms and new defected ground structures (DGSs) configurations
The DGS elements are cascaded in order to realize wider stopband with very sharp edge filter
Summary
High-performance microwave filters with minimized size and weight are playing an important role for design and fabrication of high-efficiency miniaturized microwave systems. Nonuniform transmission lines (NUTLs) play an important role in microwave circuits. Genetic algorithms are widely employed in various fields such as optimal engineering designs. They have been successfully applied to finding the global optimum in a variety of unimodal domains. Parameter control methods are classified as deterministic and adaptive. Deterministic systems employ fixed, predefined parameters for GA. Adaptive control uses feedback from the search process to find out how the parameter values change [8, 9]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Microwave Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
