Turkish Journal of Electrical Engineering and Computer Sciences
DOI
10.3906/elk-1705-362
Abstract
This paper presents an efficient modeling of Hilbert fractal inductors by improved feed forward neural network trained hybrid particle swarm optimization and gravitational search algorithm (FNNPSOGSA). The proposed model computes the effective inductance value (L) and quality factor (Q) of Hilbert fractal inductors with metal trace width, effective fractal length, frequency, and oxide thickness as input parameters. In contrast to the traditional feed forward neural network, the proposed FNNPSOGSA has been designed with fewer hidden neurons with much-enhanced learning and generalization capabilities. As a consequence, the proposed model achieves better speed and is as accurate as electromagnetic simulations. From the simulation results, it is proved that the proposed model is a good alternative for complex fractal inductor design.
Keywords
Hybrid particle swarm optimization and gravitational search algorithm (PSOGSA), high-frequency structural simulator (HFSS), inductance value (L), quality factor (Q)
First Page
2437
Last Page
2447
Recommended Citation
PADAVALA, AKHENDRA KUMAR and NISTALA, BHEEMA RAO
(2018)
"Design of an on-chip Hilbert fractal inductor using an improved feed forward neural network for Si RFICs,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 26:
No.
5, Article 23.
https://doi.org/10.3906/elk-1705-362
Available at:
https://journals.tubitak.gov.tr/elektrik/vol26/iss5/23
Included in
Computer Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons
