Nurcan Sarikaya

COMPARISON OF MAMDANI AND SUGENO FUZZY INFERENCE SYSTEM MODELS FOR RESONANT FREQUENCY CALCULATION OF RECTANGULAR MICROSTRIP ANTENNAS

Models based on fuzzy inference systems (FISs) for calculating the resonant frequency of rectangular microstrip antennas (MSAs) with thin and thick substrates are presented. Two types of FIS models, Mamdani FIS model and Sugeno FIS model, are used to compute the resonant frequency. The parameters of FIS models are determined by using various optimization algorithms. The resonant frequency results predicted by FIS models are in very good agreement with the experimental results available in the literature. When the performances of FIS models are compared with each other, the best result is obtained from the Sugeno FIS model trained by the least- squares algorithm.

Adaptive neuro-fuzzy inference system for the input resistance computation of rectangular microstrip antennas with thin and thick substrates

A new method for calculating the input resistance of electrically thin and thick rectangular microstrip patch antennas, based on the adaptive neuro-fuzzy inference system (ANFIS), is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference system and learning capability of neural networks. A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The input resistance results obtained by using the new method are in very good agreement with the experimental results available in the literature.

Concurrent Neuro-Fuzzy Systems for Resonant Frequency Computation of Rectangular, Circular, and Triangular Microstrip Antennas

A method based on concurrent neuro-fuzzy system (CNFS) is presented to calculate simultaneously the resonant frequencies of the rectangular, circular, and triangular microstrip antennas (MSAs). The CNFS comprises an artificial neural network (ANN) and an adaptive-network-based fuzzy inference system (ANFIS). In a CNFS, neural network assists the fuzzy system continuously (or vice versa) to compute the resonant frequency. The resonant frequency results of CNFS for the rectangular, circular, and triangular MSAs are in very good agreement with the experimental results available in the literature.

Resonant Frequency Calculation for Circular Microstrip Antennas with a Dielectric Cover Using Adaptive Network-Based Fuzzy Inference System Optimized by Various Algorithms

This paper presents a method based on adaptive-networkbased fuzzy inference system (ANFIS) to calculate the resonant frequency of a circular microstrip antenna (MSA) with a dielectric cover. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Six optimization algorithms, hybrid learning, least-squares, nelder-mead, genetic, differential evolution and particle swarm, are used to determine optimally the design parameters of the ANFIS. The resonant frequency results predicted by ANFIS are in very good agreement with the results reported elsewhere. When the performances of ANFIS models are compared with each other, the best result is obtained from the ANFIS model optimized by the LSQ algorithm. 280 Guney and Sarikaya

Input Resistance Calculation for Circular Microstrip Antennas Using Adaptive Neuro-Fuzzy Inference System

This paper presents a new method based on adaptive neuro-fuzzy inference system (ANFIS) to calculate the input resistance of circular microstrip patch antennas. The ANFIS is a fuzzy inference system (FIS) implemented in the framework of an adaptive fuzzy neural network. It combines the explicit knowledge representation of FIS with learning power of neural networks. A hybrid learning algorithm based on the least square approach and the backpropagation algorithm is used to optimize the parameters of ANFIS. The input resistance results predicted by ANFIS are in excellent agreement with the experimental results reported elsewhere.

Adaptive neuro-fuzzy inference system for computing the resonant frequency of electrically thin and thick rectangular microstrip antennas

A new method based on adaptive neuro-fuzzy inference system (ANFIS) for calculating the resonant frequency of rectangular microstrip antennas (MSAs) with thin and thick substrates is presented. The ANFIS has the advantages of expert knowledge of fuzzy inference systems (FISs) and learning capability of artificial neural networks (ANNs). A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The resonant frequency results obtained by using ANFIS are in excellent agreement with the experimental results reported elsewhere.

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

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.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR THE COMPUTATION OF THE CHARACTERISTIC IMPEDANCE AND THE EFFECTIVE PERMITTIVITY OF THE MICRO-COPLANAR STRIP LINE

A method based on adaptive neuro-fuzzy inference system (ANFIS) for computing the effective permittivity and the characteristic impedance of the micro-coplanar strip (MCS) line is presented. The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). A hybrid learning algorithm, which combines the least square method and the backpropagation algorithm, is used to identify the parameters of ANFIS. The effective permittivity and the characteristic impedance results obtained by using ANFIS are in good agreement with the theoretical and experimental results reported elsewhere.

Comparison of adaptive-network-based fuzzy inference system models for resonant frequency computation of circular microstrip antennas

This paper presents a method based on adaptive-network-based fuzzy inference system (ANFIS) to compute the resonant frequency of a circular microstrip antenna (MSA). The ANFIS is a class of adaptive networks which are functionally equivalent to fuzzy inference systems (FISs). Seven optimization algorithms, least-squares, nelder-mead, differential evolution, genetic, 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.