English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

General artificial neural network (ANN) analysis has been applied to optimize the lower and upper frequency limits of cutoff region of switchable microstrip circular patch antenna printed on ferrite (LiTiZn) substrate in the X-band range. The ferrite substrate offered numbers of novel magnetic and electrical characteristics including switchable and polarized radiations from a microstrip antenna with DC magnetic biasing. For particular frequency limits most of the power will be converted into mechanical waves and very little radiates into air. Under such condition the antenna become switch off, in the sense of effectively absence as radiator. In this work Radial Basis Function (RBF) networks has been applied in ANN models. Synthesis has been defined as the forward side and then analysis as the reverse side of the problem. In the RBF network, the spread value has been chosen as 0.01, which gives the best accuracy. RBF has been tested with 100 samples frequencies but trained only for particular cutoff 15 samples frequencies. The obtained optimized of value lower and upper cutoff frequency (wl & wu) are very close to the values measured through the material dispersion graph which has been plotted for perpendicular applied 1500 Oe magnetic field.

Optimization of Cutoff Frequency Limits of Microstrip Circular Patch Antenna Printed on Ferrite Substrate by Artificial Neural Network Analysis