Adaptive-network-based fuzzy inference system models for narrow aperture dimension calculation of optimum gain pyramidal horns

Title:

Adaptive-network-based fuzzy inference system models for narrow aperture dimension calculation of optimum gain pyramidal horns

Creator:

Guney , Kerim and Sarikaya, Nurcan

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:f986f37f-1ecb-4eaf-9720-f3c55f107864
uuid:f986f37f-1ecb-4eaf-9720-f3c55f107864

Subject:

Pyramidal horn, narrow aperture dimension, neuro-fuzzy inference system, ANFIS, and microwave antennas

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2008 Volume:18 | Number:5

Harvested from:

CDK

Metadata only:

false