A method based on the adaptive-network-based fuzzy inference system (ANFIS) is presented for computing the narrow aperture dimension of the pyramidal horn. Eight optimization algorithms, least-squares, hybrid learning, Nelder-Mead, genetic, differential evolution, particle swarm, simulated annealing, and clonal selection, are used to optimally determine the design parameters of the ANFIS. The narrow aperture dimension computed by using the ANFIS is used in the optimum gain pyramidal horn design. The computed gains of the designed pyramidal horns are in a very good agreement with the desired gains. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model trained by the least-squares algorithm.
In this paper, a bacterial foraging algorithm (BFA) has been used for null steering in the antenna radiation pattern by controlling only the element phases of a linear array. The BFA is an optimization algorithm based on the foraging behavior of Escherichia (E.) coli bacteria in human intestine. Numerical examples of Chebyshev pattern with the single, multiple and broad nulls imposed at the directions of interference are given to show the accuracy and flexibility of the BFA. The sensitivity of the nulling patterns due to small variations of the element phases is also investigated.