Comparison of adaptive-network-based fuzzy inference systems for bandwidth calculation of rectangular microstrip antennas

Authors:
K. Guney;N. Sarikaya
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, Erciyes University, 38039 Kayseri, Turkey;Department of Aircraft Electrical and Electronics, Civil Aviation School, Erciyes University, 38039 Kayseri, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 5
Cited 0

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence

Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Antenna Design Using Personal Computers

Antenna Design Using Personal Computers
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces

Journal of Global Optimization
Computation of resonant frequency for equilateral triangular microstrip antennas using the adaptive neuro-fuzzy inference system

International Journal of RF and Microwave Computer-Aided Engineering

Quantified Score

Hi-index	12.05

Visualization

Abstract

This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the bandwidth of a rectangular microstrip antenna (MSA). Seven optimization algorithms, least-squares, Nelder-Mead, genetic, differential evolution, hybrid learning, particle swarm, and simulated annealing are used to determine optimally the design parameters of the ANFIS. The results of the ANFIS models show better agreement with the experimental results as compared to the results of previous methods available in the literature. 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.