Summary
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.
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Adaptive-Network-Based Fuzzy Inference System Models for Narrow Aperture Dimension Calculation of Optimum Gain Pyramidal Horns
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1. IntroductionThe pyramidal horn is a highly efficient antenna that is widely used in microwave and millimeter wave systems [1], [2]. It is most commonly used as a calibration standard due to its robustness and predictability. It is also a high gain element in phased arrays and a feed element for large reflector and lens antennas. Its simplicity makes it easy to construct and use, and less expensive than most microwave antennas. Several methods [1-14], varying in accuracy and computational effort, have been presented and used to design a pyramidal horn with an optimum gain. Each of these methods has its specific advantages and disadvantages.It is well known that the values of the pyramidal horn design parameters strongly depend on the narrow and wide aperture dimensions. When the narrow aperture dimension or wide aperture dimension is accurately found, the other design parameters can be easily determined. In this paper, a method based on the ANFIS [15], [16] is presented to compute accurately the narrow aperture dimension of the pyramidal horn. The ANFIS is a class of adaptive networks, which are functionally equivalent to fuzzy inference systems (FISs). The FIS forms a useful computing framework based on the concepts of fuzzy set theory, fuzzy if-then rules, and fuzzy reasoning. The ANFIS can simulate and analyze the mapping relation between the input and output data through learning to determine optimal parameters of a given FIS. It can be trained with no need of the expert knowledge usually required for the standard fuzzy logic design. The ANFIS has the advantages of modeling the uncertainty ability of FISs and learning capability of neural networks. The ANFIS architecture requirements are fewer in number and simpler compared to neural networks. Both numerical and linguistic knowledge can be combined in...See the full content of this document
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