TY - GEN
T1 - Designing Microwave Circuits Using Genetic Algorithms Accelerated by Convolutional Neural Networks
AU - Akada, Takuma
AU - Fujimori, Kazuhiro
N1 - Publisher Copyright:
© 2021 EuMA.
PY - 2021/1/12
Y1 - 2021/1/12
N2 - Reduction of solution search time of automatic design of an RF-DC conversion circuit by Genetic Algorithms (GA) is argued in this paper. Accelerating GA by Convolutional Neural Networks (CNN) is proposed. Already simulated circuits' patterns are learned by CNN and electromagnetic simulation in GA is partially replaced by it. This approach reduces circuit generating time by 90% compared to the general GA design method to generate an RF-DC conversion circuit with the same conversion efficiency.
AB - Reduction of solution search time of automatic design of an RF-DC conversion circuit by Genetic Algorithms (GA) is argued in this paper. Accelerating GA by Convolutional Neural Networks (CNN) is proposed. Already simulated circuits' patterns are learned by CNN and electromagnetic simulation in GA is partially replaced by it. This approach reduces circuit generating time by 90% compared to the general GA design method to generate an RF-DC conversion circuit with the same conversion efficiency.
KW - convolutional neural networks
KW - genetic algorithms
UR - http://www.scopus.com/inward/record.url?scp=85100949059&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100949059&partnerID=8YFLogxK
U2 - 10.23919/EuMC48046.2021.9337992
DO - 10.23919/EuMC48046.2021.9337992
M3 - Conference contribution
AN - SCOPUS:85100949059
T3 - 2020 50th European Microwave Conference, EuMC 2020
SP - 61
EP - 64
BT - 2020 50th European Microwave Conference, EuMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 50th European Microwave Conference, EuMC 2020
Y2 - 12 January 2021 through 14 January 2021
ER -