A multiobjective optimization (MOO)
technique for a dual-band circularly polarized antenna
by using neural networks (NNs) is introduced in this
paper. In particular, the optimum antenna dimensions are
computed by modeling the problem as a multilayer feed-
forward neural network (FFNN), which is two-stage
trained with I/O pairs. The FFNN is chosen because of
its characteristic of accurate approximation and good
generalization. The data for FFNN training is obtained
by using HFSS EM simulator by varying different
geometrical parameters of the antenna. A two strip-loaded
circular aperture antenna is utilized to demonstrate the
optimization technique. The target dual bands are 835–
865 MHz and 2.3–2.35 GHz. |
